Patent ID: 12229957

DETAILED DESCRIPTION

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from raw imaging data without the validation steps and/or without advantageous processing described herein.

One advantage, for example, is the ability to utilize training sets from non-radiological sources, e.g., from tissue sample sources such as histological information, in conjunction with or independent of training sets for radiological sources, to correlate radiological imaging features to biological properties/analytes to pathologies. For example, in some embodiments, histology information may be used in training algorithms for identifying and characterizing one or more pathologies based on quantified biological properties/analytes. More specifically, biological properties/analytes which are identifiable/quantifiable in non-radiological data (such as in an invasively obtained histology data set or obtainable via gene expression profiling) may also be identified and quantified in radiological data (which is advantageously non-invasive). These biological properties/analytes may then be correlated to clinical findings on pathology using information the from non-radiological sources, for example, utilizing histological information, gene expression profiling, or other clinically rich data sets. This set of clinically correlated data may then serve as a training set or part of a training set for determining/tuning (e.g., utilizing machine learning) algorithms correlating biological properties/analytes to pathologies with a known relationship to clinical outcome. These algorithms correlating biological properties/analytes to pathologies derived utilizing non-radiological source training sets may then be applied in evaluating biological properties/analytes derived from radiological data. Thus, the systems and methods of the present disclosure may advantageously enable utilizing radiological imaging (which may advantageously be cost-effective and non-invasive) to provide surrogate measures for predicting clinical outcome or guiding treatment.

Notably, in some instances training data for non-radiological sources (such as histology information) may be more accurate/reliable than training data for radiological sources. Moreover, in some embodiments, training data from non-radiological sources may be used to augment training data from radiological sources. Thus, since better data in is likely to yield better data out, the hierarchical analytics framework disclosed advantageously improves the trainability and resulting reliability of the algorithms disclosed herein. As noted above, one key advantage is that, once trained the systems and methods of the present disclosure may enable deriving comparable clinical information to existing histological and other non-radiological diagnostic-type testing without the need not undergo invasive and/or costly procedures.

Alternatively, in some embodiments, training sets for non-radiological sources (such as non-radiological imaging sources, e.g., histological sources, and/or non-imaging sources) may be utilized in conjunction with or independent of training sets for radiological sources, e.g., in correlating image features to biological properties/analytes. For example, in some embodiments one or more biological models may be extrapolated and fitted to correlate radiological and non-radiological data. For example, histology information may be correlated with radiological information based on an underlying biological model. This correlation may advantageously enable training recognition of biological properties/analytes in radiological data utilizing non-radiological, e.g., histological information.

In some embodiments, data drawn from complementary modalities may be used, e.g., in correlating image features to biological properties/analytes from blood panels, physical FFR, and/or other sources of data.

In example embodiments one or more biological models may be extrapolated and fitted utilizing imaging data drawn from one imaging modality either correlated with and/or fused with another imaging modality or non-imaging source such as bloodwork. These biological models may advantageously correlate across and between imaging and non-imaging data sets based on the biological models. Thus, these biological models may enable the hierarchical analytics framework to utilize data from one imaging modality with another imaging modality or with a non-imaging source in identifying/quantifying one or more biological properties/analytes or identifying/characterizing one or more medical conditions.

Another advantage to the hierarchical analytics framework disclosed herein, is the ability to incorporate data from multiple same or different type data sources into the process of identifying and characterizing pathology based on imaging data. For example, in some embodiments, one or more non-imaging data sources may be used in conjunction with one or more imaging data sources in identifying and quantifying a set of biological properties/analytes. Thus, in particular, the set of biological properties/analytes may include one or more biological properties/analytes identified and/or quantified based on one or more imaging data sources, one or more biological properties/analytes identified and/or quantified based on one or more non-imaging data sources, and/or one or more biological properties/analytes identified and/or quantified based on a combination of imaging and non-imaging data sources (note that, for the purposes of the quantitative imaging systems and methods of the present disclosure the set of biological properties/analytes may generally include at least one or more biological properties/analytes identified and/or quantified based at least in part on an imaging data). The ability to augment information from an imaging data source with information from other imaging and/or non-imaging data sources in identifying and quantifying a set of biological properties/analytes adds to the robustness of the systems and methods presented herein and enables utilization of any and all relevant information in identifying and characterizing pathology.

Yet another advantage of the hierarchical analytics framework involves the ability to adjust/fine-tune data at each level, e.g., prior or subsequent to utilizing that data to assess the subsequent level (note that in some embodiments this may be an iterative process). For example, in some embodiments, information related to a set of identified and quantified biological properties/analytes may be adjusted in an a posteriori manner (e.g., after an initial identification and/or quantification thereof). Similarly, in some embodiments, information related to a set of identified and characterized pathologies may be adjusted in an a posteriori manner (e.g., after an initial identification and/or characterization thereof). These adjustments may be automatic or user based and may objective or subjective. The ability to adjust/fine-tune data at each level may advantageously improve data accountability and reliability.

In example embodiments, adjustments may be based on contextual information, which may be used to update one or more probabilities impacting a determination or quantification of a biological property/analyte. In example embodiments, contextual information for adjusting information related to a set of identified and quantified biological properties/analytes in an a posteriori manner may include patient demographics, correlations between biological properties/analytes or correlations between identified/characterized pathologies and biological properties/analytes. For example, in some instances the biological properties/analytes may be related in the sense that the identification/quantification of a first biological property/analyte may impact a probability relating the identification/quantification of a second biological property/analyte. In other instances, identification/characterization of a first pathology, e.g., based on an initial set of identified/quantified biological properties/analytes may impact a probability relating to the identification/quantification of a biological property/analyte in the initial set or even a biological property/analyte that wasn't in the first set. In further instances, pathologies may be related, e.g., wherein identification/characterization of a first pathology may impact a probability relating the identification/characterization of a first pathology. As noted above, information related to identification and quantification of biological properties/analytes and/or information related to the identification and characterization of pathologies may be updated in an iterative manner, e.g., until data convergence or thresholds/benchmarks are achieved or for a selected number of cycles.

A further advantage of the hierarchical analytics framework involves the ability to provide a user, e.g., a physician, with information relating both to a pathology as well as the underlying biology. This added context may facilitate clinical diagnosis/evaluation as well as assessing/determining next steps, e.g., therapeutic/treatment options or further diagnostics. For example, the systems and methods may be configured to determine which biological parameters/analytes relevant to the identification/quantification of one or more pathologies are most indeterminate/have the highest degree of uncertainty (e.g., by reason of lack of data or conflicting data). In such instances, specific further diagnostics may be recommended. The added context of providing a user with information relating both to a pathology as well as the underlying biology may further help the user evaluate/error check various the clinical conclusions and recommendations reached by the analytics.

A hierarchical analytics framework, as used herein, refers to an analytic framework wherein a one or more intermediary sets of data points are utilized as an intermediary processing layer or an intermediary transformation between initial set of data points and an end set of data points. This is similar to the concept of deep learning or hierarchical learning wherein algorithms are used to model higher level abstractions using multiple processing layers or otherwise utilizing multiple transformations such as multiple non-linear transformations. In general, the hierarchical analytics framework of the systems and methods of the present disclosure includes data points relating to biological properties/analytes as an intermediary processing layer or intermediary transformation between imaging data points and pathology data points, in example, embodiments, multiple processing layers or multiple transformation (e.g., as embodied by multiple levels of data points) may be included for determining each of imaging information, underlying biological information and pathology information. While example hierarchical analytic framework structures are introduced herein (e.g., with specific processing layers, transforms and datapoints), the systems and methods of the present disclosure are not limited to such implementations. Rather, any number of different types of analytic framework structures may be utilized without departing from the scope and spirit of the present disclosure.

In example embodiments, the hierarchical analytics frameworks of the subject application may be conceptualized as including a logical data layer as an intermediary between an empirical data layer (including imaging data) and a results layer (including pathology information). Whereas the empirical data layer represents directly sourced data the logical data layer advantageously adds a degree of logic and reasoning which distills this raw data into a set of useful analytes for the results layer in question. Thus, for example, empirical information from diagnostics such as raw imaging information may be advantageously distilled down to a logical information relating to a particular set of biological features which is relevant for assessing a selected pathology or group of pathologies (for example, pathologies related to an imaged region of the patient's body). In this way the biological features/analytes of the subject application can also be thought of as pathology symptoms/indicators.

The biological features/analytes of the subject application may at times be referred to herein a biomarkers. While the term “biological” or prefix “bio” is used in characterizing biological features or biomarkers this in only intended to signify that the features or markers have a degree of relevance with respect to the patient's body. For example, biological features may be anatomical, morphological, compositional, functional, chemical, biochemical, physiological, histological, genetic or any number of other types of features related to the patient's body. Example, biological features utilized by specific implementations of the systems and methods of the present disclosure (e.g., as relating to particular anatomical regions of a patient such as the vascular system, the respiratory system, organs such as the lungs, heart or kidneys, or other anatomical regions) are disclosed herein.

While example systems and methods of the present disclosure may be geared toward detecting, characterizing and treating pathologies/diseases, the application of the systems and methods of the present disclosure are not limited to pathologies/diseases but rather may more generally applicable with respect to any clinically relevant medical conditions of a patient including, e.g., syndromes, disorders, traumas, allergic reactions, etc.

In exemplary embodiments, the systems and methods of the present disclosure relate to Computer-Aided Phenotyping, e.g., by using knowledge about biology to analyze medical images to measure the differences between disease types that have been determined through research to indicate phenotypes which in turn predict outcomes. Thus, in some embodiments, characterizing pathologies may include determining phenotypes for the pathologies which may in turn determine a predictive outcome.

With initial reference toFIG.1, a schematic of an exemplary system100is depicted. There are three basic functionalities which may be provided by the system100as represented by the trainer module110, the analyzer module120and the cohort tool module130. As depicted, the analyzer module120advantageously implements a hierarchical analytics framework which first identifies and quantifies biological properties/analytes123utilizing a combination of (i) imaging features122from one or more acquired images121A of a patient50and (ii) non-imaging input data121B for a patient50and then identifies and characterizes one or more pathologies (e.g., prognostic phenotypes)124based on the quantified biological properties/analytes123. Advantageously, the analyzer module120may operate independent of ground truth or validation references by implementing one or more pre-trained, e.g., machine learned algorithms for drawing its inferences.

In example embodiments, the analyzer may include algorithms for calculating imaging features122from the acquired images121A of the patient50. Advantageously, some of the image features122may be computed on a per-voxel basis while others may be computed on a region-of-interest basis. Example non-imaging inputs121B which may be utilized along with acquired images121A may include data from laboratory systems, patient-reported symptoms, or patient history.

As noted above, the image features122and non-imaging inputs may be utilized by the analyzer module120to calculate the biological properties/analytes123. Notably, the biological properties/analytes are typically quantitative, objective properties (e.g., objectively verifiable rather than being stated as impression or appearances) that may represent e.g., a presence and degree of a marker (such as a chemical substance) or other measurements such as structure, size, or anatomic characteristics of region of interest. In example embodiments, the quantified biological properties/analytes123may be displayed or exported for direct consumption by the user, e.g., by a clinician, in addition to or independent of further processing by the analyzer module.

In example embodiments, one or more of the quantified biological properties/analytes123may be used as inputs for determining phenotype. Phenotypes are typically defined in a disease-specific manner independent of imaging, often being drawn from ex vivo pathophysiological samples for which there is documented relationship to outcome expected. In example embodiments, the analyzer module120may also provide predicted outcomes125for determined phenotypes.

It should be appreciated that example implementations of the analyzer module120are further described herein with respect to specific embodiments which follow the general description of the system100. In particular, specific imaging features, biological properties/analytes and pathologies/phenotypes are described with respect to specific medical applications such as with respect to the vascular system or with respect to the respiratory system.

With reference still toFIG.1, the cohort tool module130enables defining a cohort of patients for group analyses thereof, e.g., based on a selected set of criteria related to the cohort study in question. An example cohort analysis may be for a group of patients enrolled in a clinical trial, e.g., with the patient's further being grouped based on one or more arms of the trial for example a treatment vs. control arm. Another type of cohort analysis may be for a set of subjects for which ground truth or references exist, and this type of cohort may be further decomposed into a training set or “development” set and a test or “holdout” set. Development sets may be supported so as to train112the algorithms and models within analyzer module120, and holdout sets may be supported so as to evaluate/validate113the performance of the algorithms or models within analyzer module120.

With continued reference toFIG.1, the trainer module110may be utilized to train112the algorithms and models within analyzer module120. In particular, the trainer module110, may rely on ground truth111and/or reference annotations114so as to derive weights or models, e.g., according to established machine learning paradigms or by informing algorithm developers. In example embodiments, classification and regression models are employed which may be highly adaptable, e.g., capable of uncovering complex relationships among the predictors and the response. However, their ability to adapt to the underlying structure within the existing data can enable the models to find patterns that are not reproducible for another sample of subjects. Adapting to irreproducible structures within the existing data is commonly known as model over-fitting. To avoid building an over-fit model, a systematic approach may be applied that prevents a model from finding spurious structure and enable the end-user to have confidence that the final model will predict new samples with a similar degree of accuracy on the set of data for which the model was evaluated.

Successive training sets may be utilized to determine optimal tuning parameter(s), and a test set may be utilized to estimate an algorithm's or model's predictive performance. Training sets may be used for training each of the classifiers via randomized cross-validation. Datasets may be repeatedly split into training and testing sets and may be used to determine classification performance and model parameters. The splitting of the datasets into training and test sets occurs using a stratified or maximum dissimilarity approaches. In example embodiments a re-sampling approach (e.g. bootstrapping) may be utilized within the training set in order to obtain confidence intervals for (i) the optimal parameter estimate values, and (ii) the predictive performance of the models.

FIG.2outlines a re-sampling based model building approach200which may be utilized by the systems and methods of the present disclosure. First, at step210, a tuning parameter set may be defined. Next, at step220, for each tuning parameter set data is resampled the model is fitted and hold-out samples are predicted. At step230, Resampling estimates are combined into a performance profile. Next, at step240, final tuning parameters may be determined. Finally, at step250, the entire training set is re-fitted with the final tuning parameters. After each model has been tuned from the training set, each may be evaluated for predictive performance on the test set. Test set evaluation occur once for each model to ensure that the model building process does not over-fit the test set. For each model that is constructed, the optimal tuning parameter estimates, the re-sampled training set performance, as well as the test set performance may be reported. The values of the model parameters over randomized splits are then be compared to evaluate model stability and robustness to training data.

According to the systems and methods of the present disclosure, a number of models may be tuned for each of the biological properties/analytes (e.g., tissue types) represented in ground truth maps. Model responses may include, for example, covariance-based techniques, non-covariance based techniques, and tree based models. Depending on their construction, endpoints may have continuous and categorical responses; some of the techniques in the above categories are used for both categorical and continuous responses, while others are specific to either categorical or continuous responses. Optimal tuning parameter estimates, the re-sampled training set performance, as well as the test set performance may be reported for each model.

TABLE 1Delineate FieldRegister multiple data streams across afieldSegment organs, vessels, lesions, andother application-specific objectsReformat anatomy for specific analysesDelineate TargetRegister multiple data streams at a localeFine-grained segmentationMeasure size and/or other relevantanatomic structureExtract whole-target featuresDelineate Sub-target regionsSplit target into sub-targets accordingto applicationSub-target specific calculationsDelineate Components(Re-) Segment ComponentCalculate ReadingsVisualize Probability MapDetermine Disease SeverityDetermine PhenotypePredict OutcomeCompare Multiple Timepoints(Optional) Compare Multiple TimepointsAssess multi-focal diseaseAggregate across target lesions over awide scan field.Generate Patient ReportGenerate Patient Report

Table 1, above, provides a summary of some of the example functionalities of the analyzer module120of system100. Namely, the analyzer module120may be configured to delineate fields, for example, to register multiple data streams across a field; to segment organs, vessels, lesions and other application-specific objects; and/or to reformat/reconfigure anatomy for specific analyses. The analyzer module120may further be configured for delineating a target, for example, a lesion, in a delineated field. Delineating a target may, for example, include registering multiple data streams at a locale; conducting fine-grained segmentation; measuring size and/or other characteristics of relevant anatomic structures; and/or extracting whole-target features (e.g., biological properties/analytes characteristic of the entire target region). In some embodiments, one or more sub-target regions may also be delineated, for example, a target region may be split into sub-targets according to a particular application with sub-target specific calculations (e.g., biological properties/analytes characteristic of a sub-target region). The analyzer module120may also delineate components or relevant features (such as composition), for example, in a particular field, target or sub-target region. This may include segmenting or re-segmenting the components/features, calculating values for the segmented components/features (e.g., biological properties/analytes characteristic of the component/feature) and assigning a probability map to the readings. Next pathologies may be determined, based on the biological quantified properties/analytes, and characterized, e.g., by determining phenotype and/or predictive outcomes for the pathologies. In some embodiments, the analyzer module120may be configured to compare data across multiple timepoints, e.g., one or more of the biological components/analytes may involve a time-based quantification. In further embodiments, a wide scan field may be utilized to assess multi-focal pathologies, e.g., based on aggregate quantifications of biological properties/analytes across a plurality of targets in the delineated field. Finally, based on the forgoing analytics, the analyzer module120may be configured to generate a patient report.

A sample patient report300is depicted inFIG.3. As shown, the sample patient report300may include quantifications of biological parameters/analytes such as relating to structure310and composition320as well as data from non-imaging sources such as hemodynamics330. The sample patient report may further include visualizations340, e.g., 2D and/or 3D visualizations of imaging data as well as combined visualizations of non-imaging data such as hemodynamic data overlaid onto imaging data. Various analytics350may be displayed for assessing the biological parameters/analytes including, e.g., a visualization of one or more model(s) (e.g., a decision tree model) for determining/characterizing pathology. Patient background and identifying information may further be included. Thus, the analyzer module120of system100may advantageously provide a user, e.g., a clinician with comprehensive feedback for assessing the patient.

Advantageously the systems and methods of the present disclosure may be adapted for specific applications. Example vascular and lung applications are described in greater detail in the sections which follow (although it will be appreciated that the specific application described have general implications and interoperability with respect to numerous other applications). Table 2 provides an overview of vascular and lung related applications utilizing a hierarchical analytics framework as described herein.

TABLE 2Vascular ApplicationLung ApplicationModalityCT or MRCTIndicationAsymptomatic CASLung Cancer ScreeningCryptogenic strokeDrug therapy responseNSTEMI, CABG PatencyassessmentEvaluationCompanion-diagnostic forCompanion-diagnostic forexpensive or targeted drugsexpensive or targeted drugsDiseasesPeripheral and coronary arteryLung cancer first, then othervasculopathypulmonary diseaseBiologicalStructureSize, Shape/MarginPropertiesCompositionSolidity, HeterogeneityHemodynamicsInvasive PotentialGene Expression CorrelatesGene Expression CorrelatesExtensionUltrasound and/or multi-PET and/or multi-energy CTenergy CT

The following sections provide specific examples of quantitative biological properties/analytes that may be utilized by the systems and methods of the present disclosure with respect to vascular applications:

Anatomic Structure: Vessel structural measurements, specifically those that lead to the determination of % stenosis, have long been and remain the single most used measurements in patient care. These were initially limited to inner lumen measurements, rather than wall measurements involving both the inner and outer surfaces of the vessel wall. However, all of the major non-invasive modalities, unlike X-ray angiography, can resolve the vessel wall and with this come expanded measurements that may be achieved. The category is broad and the measurements are of objects of varying sizes, so generalizations should be made with care. A primary consideration is the limit of spatial sampling or resolution. The minimally detectable changes in wall thickness may, however, be lower than the spatial sampling by taking advantage of subtle variations in intensity levels due to partial volume effect. Additionally, stated resolutions generally refer to grid size and field of view of post-acquisition reconstructions rather than the actual resolving power of the imaging protocol, which determines the minimum feature size that can be resolved. Likewise, in-plane vs. through-plane resolutions may or may not be the same and not only the size of a given feature but as well its proportions and shape will drive the measurement accuracy. Last but not least, in some cases categorical conclusions are drawn from applying thresholds to the measurements, which may then be interpreted according to signal detection theory with the ability to optimize the trade-off between sensitivity and specificity, terms that don't otherwise refer to measurements in the normal sense.

Tissue Characteristics: The quantitative assessment of the individual constituent components of the atherosclerotic plaques, including for example lipid rich necrotic core (LRNC), fibrosis, intraplaque hemorrhage (IPH), permeability, and calcification, can provide crucial information concerning the relative structural integrity of the plaque that could aid the physician's decisions on course of medical or surgical therapy. From the imaging technology point of view, the ability to do this lies less with spatial resolution as with contrast resolution and tissue discrimination made possible by differing tissues responding to incident energy differently so as to produce a differing receive signal. Each imaging modality does this to some extent; terms in ultrasound such as “echolucency”, the CT number in Hounsfield Units, and differentiated MR intensities as a function of various sequences such as (but not limited to) T1, T2 and T2*.

Dynamic tissue behavior (e.g., Permeability): In addition to morphological features of the vessel wall/plaque, there is increasing recognition that dynamic features are valuable quantitative indicators of vessel pathology. Dynamic sequences where the acquisition is taken at multiple closely-spaced times (known as phases) expand the repertoire beyond spatially-resolved values t include temporally-resolved values which may be used for compartment modeling or other techniques to determine the tissues' dynamic response to stimulus (such as but not limited to wash-in and wash-out of contrast agent). Through the use of dynamic contrast enhanced imaging with ultrasound or MR in the carotid arteries or delayed contrast enhancement in the coronary arteries, sensitive assessments of the relative permeability (e.g., Ktrans and Vp parameters from kinetic analysis) of the microvascular networks of neoangiogenesis within the plaques of interest can be determined. In addition, these dynamic series can also aid in the differentiation between increased vascular permeability versus intraplaque hemorrhage.

Hemodynamics: The basic hemodynamic parameters of the circulation have a direct effect on the vasculopathy. Blood pressures, blood flow velocity, and vessel wall shear stress may be measured by techniques ranging from very simple oscillometry to sophisticated imaging analysis. Using common principles of fluid dynamics, calculations of vessel wall shear stress can be ascertained for different regions of the wall. In similar fashion MRI, with or without the combination of US, has been used to calculate the wall shear stress (WSS) and correlate the results with structural changes in the vessel of interest. In addition, the effects of antihypertensive drugs on hemodynamics have been followed for short and long-term studies. Thus, in example embodiments, key aspects of applying the systems and methods of the present disclosure in a vascular setting may include evaluating plaque structure and plaque composition. Evaluating plaque structure may advantageously include, e.g., lumen measurements (which improves stenosis measurement by providing area rather than only diameter measures) as well as wall measurements (e.g., wall thickness and vascular remodeling). Evaluating plaque composition may advantageously involve quantification of tissue characteristics (e.g., lipid core, fibrosis, calcification, permeability, etc.) rather than just “soft” or “hard” designations as typically found in the prior art. Tables 3 and 4, below, describe example structural calculations and tissue characteristic calculations, respectively which may be utilized by the vascular applications of the systems and methods of the present disclosure.

TABLE 3Structural calculations of vessel anatomy supported by vascularapplications of the systems and methods disclosed herein.MeasurandDescriptionType and UnitsRemodelingCalculated as the ratio of vessel area with plaqueExpressed with value less than 1 forRatioto reference vessel wall area without plaqueinward remodeling and greater than 1for outward remodeling% StenosisCalculated as the (1 − ratio of minimum lumenExpressed as percentage >0%with plaque to reference lumen without plaque) ×100 both by area and by diameter% DilationCalculated as the (ratio of maximum lumen withExpressed as percentage >0%plaque to reference lumen without plaque − 1) ×100 both by area and diameterWallCalculated by measuring the largest thickness ofExpressed in units of mmThicknesswall

TABLE 4Calculations of tissue characteristics supported by vascularapplications of the systems and methods disclosed hereinMeasurandDescriptionType and UnitsLipid CoreThe pathologic retention of lipids, particularly lipoproteins, byBurden in mm2by crossintimal/medial cells leading to progressive cell loss, cell death,section and mm3by targetdegeneration, and necrosis. It is a mixture of lipid, cellularand vesseldebris, blood and water in various concentrations.FibrosisThe pathologic and sometimes physiologic defensiveBurden in mm2by crossproduction of fibrous tissue by fibroblasts and activatedsection and mm3by targetsmooth muscle cells.and vesselCalcificationThe physiologic defensive biological process of attempting toAgatston score and burdenstabilize plaque, which has a mechanism akin to bonein mm2by cross sectionformation.and mm3by target andvesselHemorrhageA pathologic component that may contribute to theBurden in mm2by crossvulnerability of a plaque. Its role is not fully understood, but itsection and mm3by targetis believed to be a driving force in plaque progression throughand vessellipid accumulation from red blood cells.PermeabilityDescribed as endothelial and intimal permeability due toBurden in mm2by crossneovascularization, necrosis, collagen breakdown, andsection and mm3by targetinflammationand vesselThrombosisLocal coagulation or clotting of the blood in a part of theDegreecirculatory system.UlcerationDisintegration and necrosis of epithelial tissueBurden in mm2by crosssection and mm3by targetand vessel

Example systems relating to evaluating the vascular system may advantageously include/employ algorithms for evaluating vascular structure. Thus, the systems may employ, e.g., a target/vessel segment/cross-section model for segmenting the underlying structure of an imaged vessel. Advantageously a fast-marching competition filter may be applied to separate vessel segments. The systems may further be configured to handle vessel bifurcations. Image registrations may be applied utilizing Mattes mutual information (MR) or mean square error (CT) metric, rigid versor transform, LBFGSB optimizer, or the like. As noted herein, vessel segmentation may advantageously include lumen segmentation. An initial lumen segmentation may utilize a confidence connected filter (e.g., carotid, vertebral, femoral, etc.) to distinguish the lumen. Lumen segmentation may utilize MR imaging (such as a combination of normalized, e.g., inverted for dark contrast, images) or CT imaging (such as use of registered pre-contrast, post-contrast CT and 2D Gaussian distributions) to define a vessel-ness function. Various connected components may be analyzed and thresholding may be applied. Vessel segmentation may further entail outer wall segmentation (e.g., utilizing a minimum curvature (k2) flow to account for lumen irregularities). In some embodiments, an edge potential map is calculated as outward-downward gradients in both contrast and non-contrast. In example embodiments, outer wall segmentation may utilize cumulative distribution functions (incorporating prior distributions of wall thickness, e.g., from 1-2 adjoining levels) in a speed function to allow for median thickness in the absence of any other edge information. In example embodiments, Feret diameters may be employed for vessel characterization. In further embodiments, wall thickness may be calculated as the sum of the distance to lumen plus the distance to the outer wall. In further embodiments, lumen and/or wall segmentations may be done using semantic segmentation using, for example, CNNs.

Example systems relating to evaluating the vascular system may further advantageously analyze vascular composition. For example, in some embodiments, composition may be determined based on image intensity and other image features. In some embodiments, the lumen shape may be utilized, e.g., as relating to determining thrombosis. Advantageously, an analyte blob model may be employed for better analyzing composition of particular sub-regions of the vessel. We define an analyte blob to be a spatially contiguous region, in 2D, 3D, or 4D images, of one class of biological analyte. The blob model may utilize an anatomically aligned coordinate system using isocontours, e.g., in normalized radial distance from the lumen surface to the adventitial surface of the vessel wall. The model may advantageously identify one or more blobs and analyze each blobs location e.g., with respect to the overall vessel structure as well as relative to other blobs. In example embodiments, a hybrid Bayesian/Markovian network may be utilized to model a relative location of a blob. The model may advantageously account for the observed image intensity at a pixel or voxel being influenced by a local neighborhood of hidden analyte category nodes thereby accounting for partial volume and scanner point spread function (PSF). The model may further allow for dynamically delineating analyte blob boundaries from analyte probability maps during inference by the analyzer module. This is a key distinction from typical machine vision approaches, such as with superpixel approaches, that pre-compute small regions to be analyzed but are unable to dynamically adjust these regions. An iterative inference procedure may be applied that utilizes uses the current estimate of both analyte probability and blob boundaries. In some embodiments parametric modeling assumptions or kernel density estimation methods may be used to enable probability density estimates between the sparse data used to train the model.

Introduced herein is a novel model for classification of composition of vascular plaque components that removes the requirements for histology-to-radiology registration. This model still utilizes expert-annotated histology as a reference standard but the training of the model does not require registration to radiological imaging. The multi-scale model computes the statistics of each contiguous region of a given analyte type, which may be referred to as a ‘blob’. Within a cross-section through the vessel, the wall is defined by two boundaries, the inner boundary with the lumen and the outer boundary of the vessel wall, creating a donut shape in cross section. Within the donut shaped wall region, there are a discrete number of blobs (different than the default background class of normal wall tissue which is not considered to be a blob). The number of blobs is modeled as a discrete random variable. Then, each blob is assigned a label of analyte type and various shape descriptors are computed. Additionally, blobs are considered pairwise. Finally, within each blob, each pixel can produce a radiological imaging intensity value, which are modeled as independent and identically distributed (i.i.d.) samples that come from a continuously valued distribution specific to each analyte type. Note that in this last step, the parameters of the imaging intensity distributions are not part of the training process.

One key feature of this model is that it accounts for the spatial relationship of analyte blobs within the vessel and also to each other, recognizing that point-wise image features (whether from histology and/or radiology) is not the only source of information for experts to determine plaque composition. While the model allows for the ability to train without explicit histology-to-radiology registration, it could also be applied in situations where that registration is known. It is believed that statistically modeling the spatial layout of atherosclerotic plaque components for classifying unseen plaques is a novel concept.

Example techniques for estimating vessel wall composition from CT or MR images are further elaborated on in the following section. In particular, the methods may employ a multi-scale Bayesian analytic model. The basic Bayesian formulation is as follows:

P⁡(A❘I)=P⁡(I❘A)·P⁡(A)P⁡(I)⁢posterior=likelihood·priorevidence

In the context of the present disclosure, the hypothesis may be based on a multi-scale vessel wall analyte map, A, with observation combing from CT or MR image intensity information I.

As depicted inFIG.4, the multi-scale vessel wall analyte map may advantageously include wall-level segmentation410(e.g., a cross-sectional slice of the vessel), blob-level segmentation420and pixel-level segmentation430(e.g., based on individual image pixels. E.g., A=(B,C) may be defined as a map of vessel wall class labels (similar to a graph with vertices B and edges C), wherein B is a set of blobs (cross-sectionally contiguous regions of non-background wall sharing a label) and C is a set of blob couples or pairs. Bbmay be defined as a generic single blob where b∈[1 . . . nB] is an index of all blobs in A. Bbais a blob with label a. For statistical purposes, the individual blob descriptor operator DB{ } is in an low-dimensional space. Ccmay be defined as a blob pair where c∈[1. . . nB(nB−1)/2] is an index of all blob pairs in A. ccf,gis a blob pair with labels f and g. For statistical purposes, the blob pair descriptor operator DC{ } is in a low-dimensional space. A(x)=a may be defined as the class label of pixel x where a∈{‘CALC’, ‘LRNC’, ‘FIBR’, ‘IPH’, ‘background’ } (compositional characteristics). In exemplary embodiments, I(x) is the continuous valued pixel intensity at pixel x. Within each blob, I(x) are modeled as independent. Note that because the model is used to classify wall composition in 3D radiological images, the word “pixel” is used to generically denote both 2D pixels and 3D voxels

Characteristics of blob regions of like composition/structure may advantageously provide insights regarding the disease process. Each slice (e.g., cross-sectional slice) of a vessel may advantageously include a plurality of blobs. Relationships between blobs may be evaluated in a pairwise manner. The number of blobs within a cross-section is modeled as a discrete random variable and may also be of quantifiable significance. At the slice-level of segmentation, relevant characteristics (e.g., biological properties/analytes) may include a quantification of a total number of blobs and/or a number of blobs of a particular structure/composition classification; relationships between the blobs, e.g., spatial relationships such as being closer to the interior. At the blob level of segmentation, characteristics of each blob, such as structural characteristics, e.g., size and shape, as well as compositional characteristics, etc., may be evaluated serving as a biological properties/analytes. Finally, at a pixel-level of segmentation, individual pixel level analysis may be performed, e.g., based image intensity distribution.

Probability mapping of characteristics may be applied with respect to the multi-scale vessel wall analyte map depicted inFIG.4. The probability mapping may advantageously establish a vector of probabilities for every pixel with components of the vector for the probability of each class of analyte and one component for the probability of background tissue. In example embodiments, sets of probability vectors may represent mutually exclusive characteristics. Thus, each set of probability vectors representing mutually exclusive characteristics will sum to 1. For example, in some embodiments, it may be known that a pixel should fall into one and only one compositional category (e.g., a single coordinate of a vessel cannot be both fibrous and calcified). Of particular note, the probability mapping does not assume independence of analyte class between pixels. This, is because neighboring pixels or pixels within a same blob may typically have same or similar characteristics. Thus, the probability mapping accounts, as described in greater detail herein, advantageously accounts for dependence between pixels.

f(A=α) may be defined as the probability density of map α. f(A) is the probability distribution function over all vessel walls. f(DB{Ba}=β) is the probability density of descriptor vector β with label a. f(DB{Ba}) is the probability density function (pdf) of blob descriptors with label a. There is a probability distribution function for each value of a. f(B)=Πf(DB{Ba}) f(DC{Cf,g}=γ) is the probability density of pairwise descriptor vector γ with labels f and g. f(Dc{Cf,g}) is the probability density function (pdf) of pairwise blob descriptors. There is a probability distribution function for each ordered pair f,g. Thus:
f(C)=Πf(Dc{Ca})
f(A)=f(B)f(C)=Πf(Db{Ba})Πf(Dc{Ca})

P(A(x)=a) is the probability of pixel x having label a. P(A(x)) is the probability mass function (pmf) of analytes (prevalence). It can be considered a vector of probabilities at a specific pixel x or as a probability map for a specific class label value.

Note that: f(A)=P(N)·f(C)·f(B)=P(N)·Πf(Cc)·Πf(Bb)

f(Cc=γ) is the probability density of pairwise descriptor vector γ. f(Cc) is the probability density function (pdf) of pairwise blob descriptors. f(Bb=β) is the probability density of descriptor vector β. f(Bb) is the probability density function (pdf) of blob descriptors. P(A(x)=a) is the probability of pixel x having label a. P(A(x)) is the probability mass function (pmf) of analytes (prevalence in a given map). It can be considered a vector of probabilities at a specific pixel x or as a spatial probability map for a specific analyte type. P(A(x)=a|I(x)=i) is the probability of analyte given the image intensity that is our main goal to compute. P(I(x)=i|A(x)=a) is the distribution of image intensities for a given analyte.

FIG.5depicts an exemplary pixel-level probability mass function as a set of analyte probability vectors. As noted above, the following assumptions may inform the probability mass function: Completeness: in example embodiments one may assume a sufficiently small pixel must fall into at least one of the analyte classes (including a catch-all ‘background’ category) and thus the sum of probabilities sums to 1. Mutual exclusivity: a sufficiently small pixel may be assumed to belong to only one class of analyte; if there are combinations (i.e., spiculated calcium on LRNC background), then a new combination class can be created in order to retain mutual exclusivity. Non-independence: each pixel may be assumed to be highly dependent on its neighbors and the overall structure of A.

An alternative view of the analyte map is as a spatial map of probability for a given analyte. At any given point during inference, analyte blobs can be defined using the full width half max rule. Using this rule, for each local maxima of probability for that analyte a region is grown outward to a lower threshold of half the local maxima value. Note that this 50% value is a tunable parameter. Spatial regularization of blobs can be done here by performing some curvature evolution on probability maps in order to keep boundaries more realistic (smooth with few topological holes). Note that different possible putative blobs of different analyte classes may in general have spatial overlap because until one collapses the probabilities these represent alternative hypotheses for the same pixel and hence the modifier ‘putative’.

When iterative inference is terminated, there are several options for presentation of the results. First, the continuously valued probability maps can be presented directly to the user in one of several forms including but not limited to surface plots, iso-contour plots, or using image fusion similar to visualizing PET values as variation in hue and saturation on top of CT. A second alternative is to collapse the probability map at each pixel by choosing a single analyte label for each pixel. This can be done most straightforwardly by choosing the maximum a posteriori value at each pixel independently, thus creating a categorical map which could be visualized by assigning a distinct color to each analyte label and assigning either full or partial opacity on top of the radiological image. Under this second alternative, the label values might be assigned non-independently by resolving overlapping putative blobs based on a priority the probability of each blob. Hence, at a given pixel a lower priority analyte probability might be used for the label if it belongs to a higher probability blob.

FIG.6illustrates a technique for computing putative analyte blobs. In example embodiments putative blobs may have overlapping regions. Thus, it may be advantageous to apply analytical techniques to segmenting pixels by putative blobs. For a probability of a given analyte the local maxima in probability is determined. The full width half max rule may then be applied to determine discrete blobs. At any given iteration of inference, analyte blobs can be defined using the full width half max rule. Find local maxima, then region grow with a lower threshold of 0.5*max. (The 50% value could be a tunable parameter.) In some embodiments, spatial regularization of blobs may also be applied, e.g., by performing some curvature evolution on probability maps in order to keep boundaries smooth and avoid holes. Note that at this stage different possible putative blobs of different analyte classes may, in general, have spatial overlap because until probabilities are collapsed these represent alternative hypotheses. Thus, an image-level analyte map be computed, e.g., based on a collapse of the probability map function. Notably, this collapse can be determined based on either the pixel-level analyte probability map, the putative blobs or a combination of both. With respect to the pixel-level analyte probability map, collapse can be determined by for each pixel, by choosing the label with maximum probability A(x):=arg maxa P(A(x)=a). This is similar to implementation Viterbi algorithm. Basically, the highest probability for each set of mutually exclusive probabilities vectors is locked in (e.g. with analyte priorities breaking possible ties). All other probabilities in the set may then be set to zero. In some embodiments, probabilities for neighboring pixels/regions may be taken into account when collapsing data on a pixel level. With respect to putative blob level collapse, overlapping putative blobs may be resolved. In some embodiments, prioritization can be based on blob probability density f(D1{Aab}=d1). Since higher probability blobs may change shape of overlapped lower probability blob this may impact analysis of blob level characteristics. In example embodiments, the full range of probabilities may be maintained rather than collapsing the data.

In order to model the relative spatial positioning of blobs within the vessel wall, an appropriate coordinate system can be chosen in order to provide rotational-, translational-, and scale-invariance between different images. These invariances are important to the model because they allow the ability to train on one type of vessel (e.g., carotids where endarterectomy specimens are easily available) and apply the model to other vessel beds (e.g., coronary where plaque specimens are generally not available) under the assumption that the atherosclerotic process is similar across different vessel beds. For tubular objects, a natural coordinate system follows from the vessel centerline where distance along the centerline provides a longitudinal coordinate and each plane perpendicular to the centerline has polar coordinates of radial distance and angle. However, due to the variability of vessel wall geometry, especially in the diseased patients, which one may aim to analyze, an improved coordinate system may be utilized. The longitudinal distance is computed in a way so that each 3D radiological image pixel is given a value, not just along the centerline or along interpolated perpendicular planes. For a given plaque, the proximal and distal planes perpendicular to the centerline are each used to create an unsigned distance map on the original image grid, denoted P(x) and D(x), respectively where x represents the 3D coordinates. The distance map l(x)=P(x)/(P(x)+D(x)) represents the relative distance along the plaque with a value of 0 at the proximal plane and 1 at the distal plane. The direction of the 1-axis is determined by ∇l(x).

Because the geometry of the wall may be significantly non-circular, the radial distance may be defined based on the shortest distance to the inner luminal surface and the shortest distance to the outer adventitial surface. The expert-annotation of the histology images includes regions that define the lumen and the vessel (defined as the union of the lumen and vessel wall). A signed distance function can be created for each of these, L(x) and V(x), respectively. The convention is that the interior of these regions is negative so that in the wall L is positive and V is negative. The relative radial distance is computed as r(x)=L(x)/(L(x)−V(x)). It has a value of 0 at the luminal surface and 1 at the adventitial surface. The direction of the r-axis is determined by ∇r(x).

Because of the non-circular wall geometry, the normalized tangential distance may be defined as lying along iso-contours of r (and of l if processing in 3D). The direction of the t-axis is determined by ∇r×∇l. The convention is that histology slices are assumed to be viewed looking from the proximal to the distal direction so that positive l points into the image. Note that unlike the others, t does not have a natural origin since it wraps onto itself around the vessel. Thus, one can define the origin of this coordinate differently for each blob relative to the centroid of the blob.

Another wall coordinate that is used is normalized wall thickness. In some sense, this is a proxy for disease progression. Thicker wall is assumed to be due to more advanced disease. Assumption that statistical relationship of analytes changes with more advanced disease. The absolute wall thickness is easily calculated as wabs=L(x)−V(x). In order to normalize it to the range of [0-1], one may determine that maximum possible wall thickness when the lumen approaches zero size and is completely eccentric and near the outer surface. In this case the maximum diameter is the maximum Feret diameter of the vessel, Dmax. Thus, the relative wall thickness is computed as w(x)=wabs(x)/Dmax.

The degree to which the aforementioned coordinates may or may not be used in the model is in part dependent on the amount of training data available. When training data is limited, several options are available. The relative longitudinal distance may be ignored treating different sections through each plaque as though they come from the same statistical distribution. It has been observed that plaque composition changes along the longitudinal axis with more severe plaque appearance in the middle. However, instead of parameterizing the distributions by l(x), this dimension can be collapsed. Similarly, the relative wall thickness may also be collapsed. Observations have been made that certain analytes occur in “shoulder” regions of plaques where w(x) would have a middle value. However, this dimension can also be collapsed until enough training data is available.

As noted above, a vessel wall composition model may be utilized as the initial hypothesis (e.g., at the prior P(A)).FIG.7depicts normalized vessel wall coordinates for an exemplary vessel wall composition model. In the depicted model, l is relative longitudinal distance along vessel target from proximal to distal, which may be calculated, e.g., on a normalized the interval [0,1]. The longitudinal distance may be computed with 2 fast marching propagations starting from proximal and from distal planes to compute unsigned distance fields P and D wherein l=P/(P+D). 1-axis direction is ∇l. As depicted, r is normalized radial distance which may also be calculated on a normalized interval [0,1] from luminal to adventitial surface. Thus, r=L/(L+(−V)) where L is lumen signed distance field (SDF) and V is vessel SDF. r-axis direction is ∇r. Finally, t is normalized tangential distance which may be computed, e.g., on a normalized interval [−0.5,0.5]. Notably, in example embodiments there is may be no meaningful origin for the entire wall, only for individual analyte blobs (thus, t origin may be at blob centroid). The tangential distance is computed along iso-contour curves of l and of r. t-axis direction is ∇r×∇l.

FIG.9illustrates some complex vessel topologies which can be accounted for using the techniques described herein. In particular, when processing CT or MR in 3D, different branches may be advantageously analyzed separately so that the relationship between analyte blobs in separate branches are properly ignored. Thus, if a segmented view (cross-sectional slice) includes more than one lumen, one can account for this by performing a watershed transform on r in order to split up wall into domains belonging to each lumen after which each domain may be separately considered/analyzed.

As noted above, many of the coordinates and probability measurements described herein may be represented utilizing normalized scales thereby preserving scale invariance, e.g., between different sized vessels. Thus, the proposed model may advantageously be independent of absolute vessel size, under the assumption that a disease process is similar and proportional for different caliber vessels.

In some embodiments, the model may be configured to characterize concentric vs. eccentric plaque. Notably, a normalized all thickness close to 1 may indicate highly eccentric place. In further embodiments, inward vs. outward plaque characterization may be implemented. Notably, histological information on this characteristic is hindered by deformation. Thus, in some embodiments, CT and training data may be utilized to establish an algorithm for determining inward vs. outward plaque characterization.

As noted above, in example embodiments, non-imaging data, such as histology data, may be utilized as a training set for establishing algorithms linking image features to biological properties/analytes. There are however, some differences between the data types that need to be addressed in ensuring a proper correlation. For example, the following differences between histology and imaging may impact proper correlation: Carotid endarterectomy (CEA) leaves adventitia and some media behind in patient CT or MR image analysis presumed to find outer adventitial surface. (See e.g.,FIG.8depicting the margin between the plaque removed for the histology specimen relative to the outer vessel wall). Notably, scientific literature shows uncertainty of whether calcification can occur in adventitia. The following techniques may be employed to account for this difference. Histology can be dilated outward, e.g., based on an assumption that little to no analyte in the wall is left behind. Alternatively, Image segmentation can be eroded inward, e.g., based on knowledge of typical or particular margins left. For example, an average margin may be utilized. In some embodiment an average margin may be normalized a percentage of the overall diameter of the vessel. In further embodiments, histology may be used to mask the imaging (e.g., overlay, based on alignment criteria). In such embodiments it may be necessary to apply one or more transformations to the histology data to match proper alignment. Finally, in some embodiments, the difference may be ignored (which is equivalent to uniform scaling of removed plaque to entire wall). While this may induce some small error, presumably the wall left behind may be thin compared to plaque in CEA patients.

Longitudinal differences may also exist between histological data (e.g., a training set) and the imaging data as represented by the vessel wall composition model. In example embodiments, longitudinal distance may be modeled/correlated explicitly. Thus, e.g., histology slice numbering (A-G for example) can be used to roughly determine position within excised portion of plaque. This approach, however, limits analysis with respect to other slices without corresponding histology data. Thus, alternatively, in some embodiments, all histology slices may be treated as arising from the same distribution. In example embodiments, some limited regularization may still be employed along the longitudinal direction.

As noted above, normalized wall thickness, in some sense is an imperfect proxy for disease progression. In particular, a thicker wall is assumed to be due to more advanced disease, e.g. based on an assumption that statistical relationship of analytes changes with more advanced disease. Normalized wall thickness may be calculated as follows: An absolute wall thickness Tamay be determined (in mm), e.g., computed as Ta=L+(−V) where L is lumen SDF, V is vessel SDF and Dmaxis maximum Feret diameter of vessel (in mm). A relative wall thickness T may then be computed based on T=Ta/Dmax, e.g., on an interval [0,1], where 1 indicates thickest part of small lumen indicative of completely eccentric plaque. In example embodiments, probabilities may be conditioned based on wall thickness, e.g., so that the distribution of analyte blobs would depend on wall thickness. This advantageously may model differences in analyte composition over the course of disease progression.

FIG.10depicts representing an exemplary analyte blob with a distribution of normalized vessel wall coordinates. In particular, the origin oft is placed at blob centroid. (r,t) coordinates are a random vector where the location/shape is fully represented by the joint distribution of points within. This can be simplified by considering the marginal distributions (since radial and tangential shape characteristics seem relatively independent). Marginal distributions may be calculated as projections along r and t (note that l and T coordinates can also be considered). Notably, the marginal distribution in the radial direction may advantageously represent/characterize the plaque growth in concentric layers (e.g., medial layer, adventitial layer and intima layer.) Similarly, the marginal distribution in the tangential direction may advantageously represent a growth factor which may be indicative of the staging of the disease. In example embodiments, analyte blob descriptors can be computed based on the marginal distributions. For example, one can take low order statistics on the marginal distributions (or use histograms or fit parametric probability distribution functions).

In example embodiments, the following analyte blob descriptors may be used, e.g., to capture location, shape or other structural characteristics of individual blobs:Location in normalized vessel coordinatesMostly with respect to re.g., in order to distinguish between shallow/deep calcificationt-direction ignored; [optionally model l-direction]Extent in normalized vessel coordinatesIntentionally avoiding the word ‘size’ which implies an absolute measurement, whereas extent is a normalized valueLopsidedness to represent degree of asymmetry in distributionClinical significance is unclear but it may help to regularize shapes against implausible lopsided shapesAlignment to represent confinement to parallel tissue layersAnalyte blobs seem to stay within radial layers (iso-contours of r) quite well so this will help select image processed shapes that are similarWall thickness where the blob is locatedThick (i.e., advanced) plaques assumed to have different statistics than thin plaques

In some embodiments, pair-wise blob descriptors may also be utilized. For example:Relative locatione.g., if fibrosis is on the lumen side of LRNCRelative extente.g., how thick/wide is fibrosis relative to LRNCSurroundednessHow much one marginal projection falls close to the middle of the othere.g., napkin ring sign or fibrosis around LRNCRelative wall thicknessTo represent degree of ‘shoulderness’ (shoulder would be relatively less thick than central plaque body)

It is noted that higher order interactions (e.g., between three blobs or between two blobs and another feature), may also be implemented. However, consideration may be given to diminishing returns and training limitations.

The following are example quantifications of blob descriptors:

Individual blob descriptorsLocationαr= E[r]Extentβr= Var[r]βt= Var[t]Lopsidednessγr= | Skewness[r] |γt= | Skewness[t] |Alignmentδr= Kurtosis[r]δt= Kurtosis[t]ThicknessτT= E[T]Pairwise blob descriptorsRelative locationαrr= E[r2] − E[r1]αtt= E[t2] − E[t1]Relative extentβrr= Var[r2]/Var[r1]βtt= Var[t2]/Var[t1]Surroundednessεrr= |αrr| βrrεtt= |αtt| βttRelative thicknessτTT= E[T2]/E[T1]

Notably, the set of descriptors (e.g., 8-12 descriptors) form a finite shape space that a blob lives in. One can then look at the distribution of a population of blobs as a distribution in this finite space.FIG.11depicts an exemplary distribution of blob descriptors. In example embodiments the distribution of blob descriptors may be computed over the whole training set. In some embodiments, lower order statistics may be utilized on individual blob descriptors (assuming independence), e.g., Location: E[αr], Var[αr]. In other embodiments, a multi-dimensional Gaussian (mean vector+covariance matrix) analysis may be used to model the descriptors (e.g., wherein independence is not assumed). In further embodiments, if the distribution is non-normal it may be modeled with density estimation techniques.

As noted above, one can also model a number of blobs per cross section (or the number of each class), e.g., η without regard to analyte class and ηicounting number in each analyte class.FIG.14depicts frequency distribution of the total number of blobs for each histology slide. A poison regression is applied as an overly. Note that the analytic chart ofFIG.14depicts the number of blobs per cross section N without regard to analyte class (number of blobs of each analyte type is represented by B).

Summarizing the forgoing sections, in example embodiments, the overall vessel wall composition model may include the following:Per-pixel analyte prior pmf
P(A(x)=ai)=ρiIndividual blob descriptors
B1=(αr,βr,βt,γr,γt,δr,δt,τT)
B1˜N(μ1,Σ1)Pairwise blob descriptors
C2=(αrr,αtt,βrr,βtt,εrr,εtt,τTT)
C2˜N(μ2,Σ2)Number of blobsη˜Poisson(λη)wherein:

P⁡(A⁡(x)=ai)=ρif⁡(Ab)=f⁡(B1b)f⁡(A)=P⁡(η)·(∏b≠cf⁡(C2b⁢c))·∏bf⁡(Ab)

As noted above, an imaging model may serve as the likelihood (e.g., P(I\A)) for the Bayesian analytic model. A maximum likelihood estimate may then be determined. In example embodiments, this may be done considering each pixel in isolation (e.g., without regard to the prior probability of the structure in the model). Estimated analyte maps are typically smooth only because images are smooth (which is why no prior smoothing is typically performed). Independent pixel-by-pixel analysis can be done, e.g., at least up to the point of accounting for scanner PSF. The imaging model is utilized to account for imperfect imaging data. For example, imaging small components of plaque adds independent noise on top of pixel values. Moreover, the partial volume effect and scanner PSF are well known as applying to small objects. Thus, given a model (e.g., level set representation of analyte regions), simulating CT by Gaussian blurring with PSF is easy and fast. The imaging model described herein may also be applied to determine (or estimate) the distribution of true (not blurred) densities of different analytes. Notably this cannot come from typical imaging studies since these will have blurred image intensities. In some embodiments, wide variances could be used to represent the uncertainty. Alternatively, distribution parameters could be optimized from training set but the objective function would have to be based on downstream readings (of analyte areas), e.g., unless aligned histology data is available.FIG.12depicts the exemplary model for imaging data (e.g., correlating between a hidden (categorical) state (A(x)) and an observed (continuous) state (I(x)) whereby random (e.g., analyte density distribution (H(A(x))) and deterministic (e.g., scanner blur*G(x)) noise factors are accounted for. θ are the parameters of H (proportion & HU mean/variance of each analyte). θ=(τ1,μ1,σ1, . . . ,τN,μN,σN) for N different analyte classes assuming normal distributions. Note that θ are patient specific and will be estimated in an expectation maximization (EM) fashion, e.g., wherein analyte labels are the latent variables and the image is observed data.E-step: determine membership probabilities given current parametersM-step: maximize likelihood of parameters given membership probabilities

FIG.13depicts a diagram of an example Markov model/Viterbi algorithm for relating an observed state to a hidden state in an image model. In particular, the diagram depicts an observed state (gray) (observed image intensity, I(x)) and a hidden state (white) (pure analyte intensity, H(A(x))) which can be modeled either with empirical histogram or with Gaussian or boxcar probability distribution function. PSF of imaging system is modeled as Gaussian, G(x). Thus,
I(x)=G(x)*H(A(x))

It is noted that a Viterbi-like algorithm could apply here but convolution would replace emission probabilities H could be modeled as Gaussian or uniform.

As noted above, one portion of the inference procedure is based upon expectation maximization (EM). In a typical application of EM, data points are modeled as belonging to one of several classes, which is unknown. Each data point has a feature vector and for each class, this feature vector may be modeled with a parametric distribution such as a multidimensional Gaussian, represented by a mean vector and a covariance matrix. In the context of the model presented herein, a straightforward EM implementation would work as follows:

L⁡(θ;I)=∏x=1Np⁢i⁢x⁢e⁢l⁢s∑a=1Nanalytesτa⁢G⁡(I⁡(x);μa,σa)⁢where⁢G⁢is⁢Gaussian⁢functionL⁡(θ;I,A)=p⁡(I,A❘θ)=∏x=1Np⁢i⁢x⁢e⁢l⁢s∑a=1Nanalytesδa,A⁡(x)⁢τa⁢G⁡(I⁡(x);μa,σa)⁢where⁢δ⁢is⁢Kronecker⁢delta=exp⁢{∑x=1Npixels∑a=1Nanalytesδa,A⁡(x)[ln⁢τa-ln⁡(2⁢π⁢σa2)2-(I⁡(x)-μa)22⁢σa2]}Tj,x(t):=P⁡(A⁡(x)=j❘I=I⁡(x);θ(t))=τj(t)⁢G⁡(I⁡(x);μj(t),σj(t))∑a=1Nanalytesτa(t)⁢G⁡(I⁡(x);μa(t),σa(t))⁢(membership⁢probabilities)Q⁡(θ|θ(t))=E[ln⁢L⁡(θ;I,A)]=E[ln⁢∏x=1NpixelsL⁡(θ;I⁡(x),A⁡(x))]=∑x=1Np⁢i⁢x⁢e⁢l⁢sE[ln⁢L⁡(θ;I⁡(x),A⁡(x))]=∑a=1Nanalytes∑x=1Np⁢i⁢x⁢e⁢l⁢sTa,x(t)[ln⁢τa-1⁢ln⁡(2⁢π⁢σa2)2-(I⁡(x)-μa)22⁢σa2]τ(t+1)=arg⁢maxτ⁢{∑a=1Nanalytes([∑x=1Np⁢i⁢x⁢e⁢l⁢sTa,x(t)]⁢ln⁢τa)}τj(t+1)=1Npixels⁢∑x=1Np⁢i⁢x⁢e⁢l⁢sTj,x(t)(μj(t+1),σj(t+1))=arg⁢maxμ,σ⁢{∑a=1Nanalytes[∑x=1Np⁢i⁢x⁢e⁢l⁢sTa,x(t)][-1⁢ln⁡(2⁢π⁢σa2)2-(I⁡(x)-μa)22⁢σa2])}μj(t+1)=∑x=1NpixelsTj,I⁡(x)(t)⁢I⁡(x)∑x=1NpixelsTj,I⁡(x)(t)σj(t+1)=∑x=1NpixelsTj,I⁡(x)(t)(I⁡(x)-μj(t+1))2∑x=1NpixelsTj,I⁡(x)(t)

The main problem with this simple model is that it doesn't code any higher order structure to the pixels. There is no prior probability associated with more realistic arrangements of pixels. Only tau determines the proportion of analyte classes. Thus, one can use the tau variable to insert in the blob prior probability model, in particular at the step of updating membership probabilities.

Thus, a modified Bayesian inference procedure may be applied with a much more sophisticated Bayesian prior. In the basic EM implementation, there is no real prior distribution. The variable tau represents the a priori relative proportion of each class but even this variable is unspecified and estimated during the inference procedure. Thus, there is no a priori belief about the distribution of classes in the basic EM model. In our model, the model prior is represented by the multi-scale analyte model. Tau becomes a function of position (and other variables), not just a global proportion.

L⁡(θ;I,A)=f⁡(I,A|θ)=f⁡(A)⁢f⁡(I|A,θ)=f⁡(A)⁢∏x=1NpixelsG⁡(I⁡(x);μA⁡(x),σA⁡(x))=f⁡(A)⁢exp⁢{∑x=1Npixels-ln⁡(2⁢π⁢σa2)2-(I⁡(x)-μa)22⁢σa2}Q⁡(θ|θ(t))=E[ln⁢L⁡(θ;I,A)]=E[ln⁢f⁡(A)+∑x=1Npixels-ln⁡(2⁢π⁢σa2)2-(I⁡(x)-μa)22⁢σa2]=E[ln⁢f⁡(A)]+∑a=1Nanalytes∑x=1NpixelsTa,x(t)[-1⁢ln⁡(2⁢π⁢σa2)2-(I⁡(x)-μa)22⁢σa2](μj(t+1),σj(t+1))=arg⁢maxμ,σ⁢{∑a=1Nanalytes([∑x=1NpixelsTa,x(t)][-1⁢ln⁡(2⁢π⁢σa2)2-(I⁡(x)-μa)22⁢σa2])}μj(t+1)=∑x=1NpixelsTj,I⁡(x)(t)⁢I⁡(x)∑x=1NpixelsTj,I⁡(x)(t)σj(t+1)=∑x=1NpixelsTj,I⁡(x)(t)(I⁡(x)-μj(t+1))2∑x=1NpixelsTj,I⁡(x)(t)

The membership probability function is defined as follows:

f⁡(I,A|θ)=f⁡(A)⁢f⁡(I|A,θ)=f⁡(A)⁢∏x=1NpixelsG⁡(I⁡(x);μA⁡(x),σA⁡(x))f⁡(A|I,θ)=1Z⁢f⁡(A)⁢f⁡(I|A,θ)P⁡(A⁡(x)=j|I⁡(x)=i,θ)=1Z⁢P⁡(A⁡(x)=j)⁢f⁡(I⁡(x)=i|A⁡(x)=j,θ)Tj,x(t):=P⁡(A⁡(x)(t)=j|I⁡(x)=i,θ)Tj,x(t):=P⁡(A⁡(x)(t)=j)⁢G⁡(I⁡(x);μj(t),σj(t))∑a=1NanalytesP⁡(A⁡(x)(t)=a)⁢G⁡(I⁡(x);μa(t),σa(t))⁢(membership⁢probabilities)Tj,x(t):=1Z⁢Emodels[P⁡(A⁡(x)=j)⁢P⁡(I⁡(x)=i|A⁡(x)=j,θ)]:=1Z⁢∑a∈modelsP⁡(A=α)⁢P⁡(A⁡(x)=j)⁢P⁡(I⁡(x)=i|A⁡(x)=j,θ):=1Z⁢∑a∈modelsP⁡(N)⁢∏f⁡(Cc)⁢∏f⁡(Bb)⁢P⁡(A⁡(x)=j)⁢P⁡(I⁡(x)=i❘A⁡(x)=j,θ)

The inference algorithm is as follows. At each step of iteration, the membership probability map is initialized to zero so that all classes have zero probability. Then for all possible model configurations, the membership probability map may be added to as follows:
Tj,x(t)+=P(N(t))Πf(Cc(t))Πf(Bb(t))P(A(x)(t)=j)P(I(x)=i|A(x)(t)=j,θ)

Finally, the probability vector may be normalized at each pixel in the membership probability map to restore the completeness assumption. Advantageously one can iterate over all model configurations. This is done by sequentially considering values for N from 0 to a relatively low value, for instance 9, at which point extremely few sections have ever been observed to have as many blobs. For each value of N, one can examine different putative blob configurations. The putative blobs may be thresholded to a small number (N) based on their individual blob probabilities. Then, all of the permutations of N blobs are considered. Thus, one can simultaneously considering all of the most likely blob configurations and weighting each model by its prior probability. This procedure is obviously an approximate inference scheme since the full space of multi-scale model configurations may not be considered. One can assume, however, that by considering the most likely (in terms of both N and blobs), a good approximation is achieved. This procedure also assumes that the weighted average of the most likely configurations provides a good estimate at each individual pixel. Another alternative is to perform a constrained search of model configurations and select the highest likelihood model as the MAP (maximum a posteriori) estimate.

Further exemplary statistical models (e.g., the posterior P(A\I)) are also described herein. In a CT angiography the following information may be available:IntensityCT Hounsfield units or MR intensitiesPossibly other imaging featuresPosition relative to anatomyWhere in the plaque a pixel isNeighboring pixelsE.g., for smoothing contours through level setsPosterior probability may be computed as:
P(A|I)∝P(I|A)·P(A)

Thus, the following image information may influence analyte probability, Ai(x)I(x) is observed image intensity (possibly a vector)T(x) is observed relative wall thickness from image segmentationF(x) are CT image featuresS(x) are features of vessel wall shape (e.g., luminal bulge)

In some embodiments a Metropolis-Hastings like approach may be utilized. In other embodiments a maximum a posteriori approach may be applied.

The following are example algorithmic possibility for a statistical analysis model. In some embodiments, the model may utilize Belief propagation (AKA max sum, max product, sum product messaging). Thus, for example a Viterbi (HMM) type approach may be utilized, e.g., wherein, hidden states are the analyte assignments, A, observed states are the image intensities, I. This approach may advantageously find a MAP estimate may be argmax P(A|I). In some embodiments a soft output Viterbi algorithm (SOVA) may be utilized. Note that reliability of each decision may be indicated by difference between chosen (survivor) path and discarded path. Thus, this could indicate reliability of each pixel analyte classification. In further example embodiments a forward/backward Baum-Welch (HMM) approach may be utilized. For example, one can compute most likely state at any point in time but not the most likely sequence (see Viterbi).

Another possible technique is the Metropolis-Hastings (MCMC) approach, e.g., wherein one repeatedly samples A and weights by likelihood and prior. In some embodiments, a simple MRF version for sampling may be utilized. Note that it may be particularly advantageous to sample the posterior directly. In example embodiments, one can build up per-pixel histograms of analyte class.

Other algorithm possibilities include applying a Gibbs Sampler, Variational Bayes (similar to EM), Mean field approximation, a Kalman filter, or other techniques.

As noted above, in some embodiments an Expectation Maximization (EM) posterior approach may be utilized. Under this approach, observed data X is the imaging values, unknown parameters θ are due to the analyte map (but not including analyte probabilities) and latent variable Z is the analyte probability vector. One key feature of this approach is that it enables iterating between estimating class membership (Z) and model parameters (θ) since they each depend on each other. However, since the analyte map separates out analyte probabilities, the approach may be modified such that the current class membership doesn't have to influence the model parameters (since these are learned this during a training step). Thus, EM basically learning the model parameters as it iterates through the current data. Advantageously, exemplary implementation of the EM approach iteratively compute maximum likelihood but assumes a flat prior.

Techniques are also provided herein for representing longitudinal covariance. Due to wide spacing of histology slices (e.g., 4 mm), sampling may not faithfully capture the longitudinal variation in analytes. However, 3D image analysis is typically performed and presumably there is some true longitudinal covariance. The problem is that histological information typically isn't provided for longitudinal covariance. Nonetheless the exemplary statistical models disclosed herein may reflect a slow variation in longitudinal direction.

In some embodiments, a Markov model/chain may be applied.FIG.15depicts exemplary implantation of a 1D Markov chain for Text/DNA. Conventionally, when applied to images in MRF Markov chains are typical as low order as possible. A higher order chain may be advantageous, however, due to conditional independence (Markov property). Otherwise the data may be too scrambled to be of value. This is demonstrated by the 1D sampling of an exemplary Markov chain as applied to text:Uniform probability sampling output:earryjnv anr jakroyvnbqkrxtgashqtzifzstqaqwgktlfgidmxxaxmmhzmgbya mjgxnlyattvc rwpsszwfhimovkvgknlgddou nmytnxpvdescbg k syfdhwqdrj jmcovoyodzkcofmlycehpcqpuflje xkcykcwbdaifculiluyqerxfwlmpvtlyqkv0-order Markov chain output:ooyusdii eltgotoroo tih ohnnattti gyagditghreay nm roefnnasos r naa euuecocrrfca ayas el s yba anoropnn laeo piileo hssiod idlif beeghec ebnnioouhuehinely neiis cnitcwasohs ooglpyocp h trog l1storder Markov chain output:icke inginatenc blof ade and jalorghe y at helmin by hem owery fa st sin r d n cke s t w anks hinioro e orin en s ar whes ore jot j whede chrve blan ted sesourethegebe inaberens s ichath fle watt o2ndorder Markov chain output:he ton th a s my caroodif flows an the er ity thayertione wil ha m othenre re creara quichow mushing whe so mosing bloack abeenem used she sighembs inglis day p wer wharon the graiddid wor thad k3rdorder Markov chain output:es in angull o shoppinjust stees ther a kercourats allech is hote ternal liked be weavy because in coy mrs hand room him rolio and ceran in that he mound a dishine when what to bitcho way forgot p

FIG.16depicts an example first order Markov chain for a text probability table. Note that such tables are exponentially sized in terms of order:D=order of Markov chainN=number of lettersSize=ND

Thus, higher order leads to problems with dimensionality. Advantageously histology samples have a very high resolution. However, since histology samples are not statistically independent, this may lead to overfitting as later described in greater detail. In general, the more conditional dependence that is modeled, the more predictive the model can be.

In example embodiments, a 2D Markov random field (MRF) may be used for pixel values instead of a 1D sequence such as for letters.FIG.17depicts conditional dependence of a first pixel (black) based on its neighboring pixels (gray). In example embodiments cliques may make use symmetry to reduce the number of dependencies in half. In some embodiments, the values of pixels could be simple image intensities or could be probability values for classification problems. Problems exist with typical MRF use. Conventional MRF almost always is limited to the nearest neighbor pixels providing conditional dependence which greatly reduces the specificity of the probability space represented; usually just black/white blobs for very general purpose segmentation/filtering; extremely short range dependencies. However, whereas pixels are highly discretized a blob just missing one pixel and falling in the next may completely change the probability distribution. Thus, a real image structure is much more continuous than is typically accounted for using MRF.

For this reasons the systems and methods of the present disclosure may advantageously utilize an inference procedure, e.g., a Bayes type rule of Posterior α Likelihood×Prior (P(A/Iα a P(I/A)×P(A)). Using a crossword type analogy, the inference procedure implemented by the systems and methods of the subject application is a bit like trying to OCR a crossword puzzle from a noisy scan. Knowledge (even imperfect knowledge of several squares may help inform an unknown square in the crossword puzzle. Efficiently is improved even more by considering both vertical and horizontal direction simultaneously. In example embodiments, the inference procedure may be heuristic. For example, one can initialize with uninformed prior, then, solve the easier ones first, which gives you clues about the harder ones which are solved later. Thus, relatively easy to detect biological properties such as dense calcium may inform the existence of other harder to detect analytes such as lipids. Each step of the inference procedure may narrow the probability distributions for unsolved pixels.

As noted above a high order Markov chain is preferable to obtain usable data. The disadvantage of utilizing a higher order Markov approach is that there may not be enough data to inform the inference process. In example embodiments, this issue may be addressed by utilizing density estimation methods such as Parzen windowing or utilizing kriging techniques.

To form an inference procedure, one may initialize with unconditional prior probabilities of analytes and then use a highest level of evidence to start narrowing down probabilities. For example, in some embodiments, an uncertain width may be associate with each analyte probability estimate. In other embodiments, closeness to 1/N may represent such uncertainty.

Notably, the term “Markov” is used loosely herein since the proposed Markov implementations are not memoryless but rather are explicitly trying to model long range (spatial) dependencies.

Because the CT resolution is low compared to histology and plaque anatomy, in some embodiments it may be preferable to utilize a continuous space (time) Markov model rather than discrete space (time). This may work well with the level set representation of probability maps since they naturally work well with sub-pixel interpolation. Discrete analyte states makes the model a discrete space model. However, if one represents continuous probabilities rather than analyte presence/absence, then it becomes a continuous space model.

Turning to lung based applications, table 5 below depicts exemplary biological properties/analytes which may utilized with respect to a hierarchical analytics framework for such applications.

TABLE 5Biologically-objective measurands Supported by lung based applicationsCategoryDescriptionReadingsUnits/CategoriesSizeThe size of the lesionVolume (lesion, solidmm{circumflex over ( )}3portion, ground-glassportion)Longest diameter andmmperpendicular (lesion, solidportion, ground-glassportion)Shape/MarginOverall shape of theShapesphericity (unitless: round = 1,lesion and descriptionsoval ~0.5, line = 0)/lobulated -of its border which mayirregular/cavitary,indicate certain cancersspeculation, notch/cutor diseases (possiblyMarginTumor margin scale (HU)including fibroticTumor margin windowscarring)(HU/mm)TopologyEuler NumberSolidityMean development ofVolume % solid of Lesion%cell types or lack(C/T ratio)thereof that make up theVolume % ground-glass of%lesion (differentiation,Lesionorganization)Solid densityg/mlGround glass densityg/mlMass of solidgMass of ground glassgHeterogeneityCovariance andSD (variation of solidg/mldevelopment of celldensity)types or lack thereofSD (variation of groundg/mlthat make up the lesionglass)(differentiation,PatternNonsolid or ground-glassorganization)opacity (pure GGN)/perifissural/part-solid (mixedGGN)/solidSolid portion patternRadial intensity distribution 1stand 2ndorder statistics (Central/central with ring/diffuse/peripheral)Spatial coherence (texture,NSM (non-spatial methods);“clumpiness”, localizedSGLM (spatial gray-levelheterogeneity)methods) e.g., Haralick; fractalanalysis (FA): Lacunarity,average local variance,variance of local variance,average of local average; filters& transforms (F&T) e.g.,GaborInvasiveMeasure of Lesion'sPleural contact length (AKAmmPotentialinvasive extent orarch distance)potential extentPleural contact length-to-unitlessmaximum lesion diameterPleural InvolvementDisplacement from expectedlocationLobe LocationUpper/middle/lower lobe//right/leftLobe centralityunitless (1 = lobe center, 0 =lobe boundary)Airway Involvement/aircategorybronchogramVascular changesDilated/rigid/convergent/tortuousCalcificationResponse to injuriousVolumemm{circumflex over ( )}3agent (dystrophic) orVolume % of Lesion%caused by derangedDistributionCentral/peripheral/diffusemetabolism (metastatic)Patternamorphous/punctuate/reticular/popcorn/laminatedCell MetabolismMeasures of cellUptakeSUV (unitless), % ID/gmetabolismGlycolytc volume<each non-Change assessedPairwise arithmeticIn units of measurandcategoricalbetween as few as 2 butdifferencemeasurandarbitrarily manyPairwise ratiounitlessabove>timepointsPairwise doubling timedays/weeks/monthsPolynomial fit coefficientsNon-arithmetic changeassessment with registration,e.g., vascular changes<each non-Assessed over multipleTotal Tumor Burdenmm{circumflex over ( )}3categoricaltargets according toTumor Numberunitlessmeasurandresponse criteria, e.g.,MultilobarTrue/falseabove>RECIST, WHO, etc.Lymph Node statuscategoryMetastasiscategoryResponsecategory

In particular, systems may be configured to detect lung lesions. Thus, an exemplary system may be configured for whole lung segmentation. In some embodiments, this may involve use of minimum curvature evolution to solve juxtapleural lesion problems. In some embodiments, the system may implement lung component analysis (vessel, fissure, bronchi, lesion etc.). Advantageously a Hessian filter may be utilized to facilitate lung component analysis. In some embodiments lung component analysis may further include pleural involvement, e.g., as a function of fissure geometry. In further embodiments, attachment to anatomic structures may also be considered. In addition to lung component analysis, separate analysis of ground glass vs. solid stated may also be applied. This may include determination of geometric features, such as volume, diameter, sphericity, image features, such as density and mass, and fractal analysis.

Fractal analysis may be used to infer lepidic growth patterns. In order to perform fractal analysis on very small regions of interest, our method adaptively modifies the support for convolution kernels to limit them to the region of interest (i.e., lung nodule). Intersecting vessels/bronchi as well as non-lesion feature may be masked out for the purposes of fractal analysis. This is done by applying IIR Gaussian filters over masked local neighborhoods and normalizing with IIR blurred binary masking. In some embodiments, fractal analysis may further include determining lacunarity (based on variance of the local mean). This may be applied with respect to lung lesions, subparts of lesions. In example embodiments, IIR Gaussian filters or circular neighborhoods may be applied. In some embodiments IIR may be utilized to compute variance. Average of local variance (AVL) may also be computed, e.g., as applied to lung lesions. Likewise, a variance of local variance may be calculated.

In example embodiments, both lesion structure and composition may be calculated. Advantageously calculating lesion structure may utilize full volumetry of thin sections thereby improving on calculating size measurement change. Measurements such as sub-solid and ground glass opacity (GGO) volume may also be determined as part of assessing lesion structure. Turning to lesion composition, tissue characteristics such as consolidation, invasion, proximity and perfusion may be calculated e.g., thereby reducing false positive rate relative to conventional analytics.

With reference now toFIG.18, a further exemplary hierarchical analytics framework1800for the systems of the present disclosure is depicted.FIG.18may be understood as an elaboration ofFIG.1elucidating greater detail with respect to exemplary intermediate processing layers of the hierarchical inference system. Advantageously the hierarchical inferences still flow from imaging data1810to underlying biological information1820to clinical disease1830. Notably, however, the framework1800includes multiple levels of data points for processing imaging data in order to determine biological properties/analytes. At a pre-processing level1812, physical parameters, registrations transformations and region segmentations may be determined. This preprocessed imaging information may then be utilized to extract imaging features at the next level of data points1814such as intensity features, shape, texture, temporal characteristics, and the like. Extracted image features may next utilized at level1816to fit one or more biological models to the imaged anatomy. Example models may include a Bayes/Markov net lesion substructure, a fractal growth model, or other models such as described herein. The biological model may advantageously act as a bridge for correlating imaging features to underlying biological properties/analytes at level1822. Example biological properties/analytes include anatomic structure, tissue composition, biological function, gene expression correlates, and the like. Finally, at level1824the biological properties/analytes may be utilized to determine clinical findings related to the pathology including, e.g., related to disease subtype, prognosis, decision support and the like.

FIG.19is an example application of phenotyping purpose in directing vascular therapy, using the Stary plaque typing system adopted by the AHA as an underlay with in vivo determined types shown in color overlays. The left panel shows an example that labels according to the likely dynamic behavior of the plaque lesion based on its physical characteristics, and the right panel illustrates an example which uses the classification result for directing patient treatment. An example mapping is [‘I’,‘II’,‘III’,‘IV’,‘V’,‘VI’,‘VII’,‘VIII’] yielding class_map=[Subclinical, Subclinical, Subclinical, Subclinical, Unstable, Unstable, Stable, Stable]. This method is not tied to Stary, e.g., the Virmani system [‘Calcified nodule’, ‘CTO’, ‘FA’, ‘FCP’, ‘Healed Plaque Rupture’, ‘PIT’, ‘IPH’, ‘Rupture’, ‘TCFA’, ‘ULC’] has been used with class_map=[Stable, Stable, Stable, Stable, Stable, Stable, Unstable, Unstable, Unstable, Unstable], and other typing systems may yield similarly high performance. In example embodiments, the systems and methods of the present disclosure may merge disparate typing systems, the class map may be changed, or other variations. For FFR phenotypes, values such as normal or abnormal may be used, and/or numbers may be used, to facilitate comparison with physical FFR for example.

FIG.20is example for a different disease, e.g., lung cancer. In this example, the subtypes of masses are determined so as to direct the most likely beneficial treatment for the patient based on the manifest phenotype.

CNNs are expected to perform better than readings-vector classification because CNNs contain filters which extract spatial context which isn't included in (only) analyte area measurements. It may be practical to use a CNN despite the reduced training set because1) there are relatively few classes corresponding to significantly different treatment alternatives (rather than being fully granular as might be done in research assays necessitating ex vivo tissue), e.g. three phenotypes for the classification problem, three risk levels for the outcome prediction/risk stratification problem, so the problem is generally easier.2) the processing of analyte regions into false color regions, e.g., by level sets or other algorithm classes, performs a substantial portion of the image interpretation by generating the segmentations and presenting the classifier with a simplified, but considerably enriched data set. Measurable pipeline stages reduce the dimensionality of the data (reducing the complexity of the problem that the CNN must solve) while also providing verifiable intermediate values which can increase confidence in the overall pipeline.3) re-formatting the data using a normalized coordinate system removes noise variation due to variables that do not have a substantial impact on the classification, e.g., vessel size in the plaque phenotyping example.

To test this idea a pipeline was built consisting of three stages:1) semantic segmentation to identify which regions of the biomass fall into certain classes2) spatial unwrapping to convert the vein/artery cross section into a rectangle, and3) a trained CNN to read the annotated rectangles and identify which class (stable or unstable) it pertains to.

Without loss of generality, example systems and methods described herein may apply spatial unwrapping (for example, training and testing CNNs with (unwrapped dataset) and without (donut dataset) spatial unwrapping). Unwrapping was observed to improve the validation accuracy

Semantic Segmentation and Spatial Unwrapping:

First, the image volume is preprocessed. This may include target initialization, normalization, and other pre-processing such as deblurring or restoring to form a region of interest containing a physiological target that is to be phenotyped. Said region is a volume composed of cross sections through that volume. Body site is either automatically determined or is provided explicitly by user. Targets for body sites that are tubular in nature are accompanied with a centerline. Centerlines, when present, can branch. Branches can be labelled either automatically or by user. Generalizations on centerline concept may be represented for anatomy that is not tubular but which benefit by some structural directionality, e.g., regions of a tumor. In any case, a centroid is determined for each cross section in the volume. For tubular structures this will be the center of the channel, e.g., the lumen of a vessel. For lesions this will be the center of mass of the tumor.

FIG.21is representative of an exemplary image pre-processing step, in this case deblurring or restoring using a patient-specific point spread determination algorithm to mitigate artifacts or image limitations that result from the image formation process that may decrease the ability to determine characteristics predictive of the phenotype. The figure demonstrates a portion of the radiology analysis application analysis of a plaque from CT. Shown here are the deblurred or restored images that are a result of iteratively fitting a physical model of the scanner point spread function with regularizing assumptions about the true latent density of different regions of the image. This figure is included so as to illustrate that a variety of image processing operations may be performed so as to aid in the ability to perform quantitative steps, and in no way to indicate that this method is needed for the specific invention in this disclosure but rather being exemplary of steps which may be taken to improve overall performance.

The (optionally deblurred or restored) image is represented in a Cartesian data set where x is used to represent how far from centroid, y represents a rotational theta, and z represents the cross section. One such Cartesian set will be formed per branch or region. When multiple sets are used, a “null” value will be used for overlapping regions, that is, each physical voxel will be represented only once across the sets, in such a way as to geometrically fit together. Each data set will be paired with an additional data set with sub-regions labelled by objectively verifiable tissue composition (see, e.g.,FIG.36). Example labels for vascular tissue can be lumen, calcification, LRNC, etc. Example labels for lesions could be necrotic, neovascularized, etc. These labels can be validated objectively, e.g. by histology (see, e.g.,FIG.37). Paired data sets will used as input to a training step to build a convolutional neural network. Two levels of analysis are supported, one at an individual cross-section level, optionally where the output varies continuously across adjacent cross-sections, and a second at the volume level (where individual cross-sections may be thought of as still frames, and the vessel tree traversal could be considered as analogous to movies).

Exemplary CNN Design:

AlexNet is a CNN, which competed in the ImageNet Large Scale Visual Recognition Challenge in 2012. The network achieved a top-5 error of 15.3%. AlexNet was designed by the SuperVision group, consisting of Alex Krizhevsky, Geoffrey Hinton, and Ilya Sutskever at U Toronto at the time. AlexNet was trained from scratch to classify an independent set of images (not used in training and validation steps during the network training). For the unwrapped data an AlexNet style network with 400×200 pixel input was used, and the donut network is AlexNet style with 280×280 pixel input (roughly the same resolution but different aspect ratio). All of the convolutional filter values were initialized with weights taken from AlexNet trained on the ImageNet dataset. While the ImageNet dataset is a natural image dataset, this simply serves as an effective method of weight initialization. Once training begins, all weights are adjusted to better fit the new task. Most of the training schedule was taken directly from the open source AlexNet implementation, but some adjustment was needed. Specifically, the base learning rate was reduced to 0.001 (solver.prototxt) and the batch size was reduced to 32 (train_val.prototxt) for both the AlexNet-donut and AlexNet-unwrapped networks. All models were trained to 10,000 iterations and were compared to snapshots when trained till just 2,000 iterations. While a more in depth study on overfitting could be done, it was generally found that both training and validation error decreased between 2 k and 10 k iterations.

Alternative featurizers (prefixes) could include:ResNet—https://arxiv.org/abs/1512.03385GoogLeNet—https://www.cs.unc.edu/˜wliu/papers/GoogLeNet.pdfResNext—https://arxiv.org/abs/1611.05431ShuffleNet V2—https://arxiv.org/abs/1807.11164MobileNet V2—https://arxiv.org/abs/1801.04381

Run-time optimizations such as frame-to-frame redundancy between cross-sections (sometimes referred to as “temporal” redundancy, but in our case, being a form of inter-cross-section redundancy) could be leveraged to save on computation (e.g., http://arxiv.org/abs/1803.06312). Many optimizations for training or inference may be implemented.

In example test implementations, AlexNet was trained to classify an independent set of images between two categories of clinical significance, e.g., ‘unstable’ plaques and ‘stable’ plaques, based on histology ground truth plaque types of V and VI, while the latter includes plaque types VII and VIII following the industry de-facto standard plaque classification nomenclature accepted by the American Heart Association (AHA), and on a related but distinct typing system by Virmani

Without loss of generality, in illustrated examples, both total accuracy and a confusion matrix were be utilized to assess performance. This formalism was based on the notion of computing four possibilities in a binary classification system: true positives, true negatives, false positives and false negatives. In example embodiments, other outcome variables can be used, however, for example, one can utilize sensitivity and specificity as outcome variables, or the F1 score (the harmonic mean of precision and sensitivity). Alternatively, an AUC characteristic can be computed for a binary classifier. Furthermore, classifiers need not be binary based. For example, in some embodiments, classifiers may sort based on more than two possible states.

Dataset Augmentation:

Physician annotated data is expensive, so it is desirable to artificially increase medical datasets (e.g., for use in training and/or validation). Two different augmentation techniques were used in example embodiments described herein. Donuts were horizontally flipped randomly, as well as rotated to a random angle from 0 to 360. The resulting rotated donut was then cropped to the range in which the donut was present, and then padded with black pixels to fill the image to have a square aspect ratio. The result was then scaled to the 280×280 size and saved to a PNG.

The unwrapped dataset was augmented by randomly horizontally flipping, and then “scrolled” by a random number of pixels in the range from 0 to the width of the image. The result was then scaled to the 400×200 size and saved to a PNG.

Both datasets were increased by a factor of 15, meaning that the total number of images after augmentation is 15 times the original number. Class normalization was implemented, meaning that the final dataset has roughly the same number of images pertaining to each class. This is important as the original number of images for each class might be different, thus biasing the classifier to the class with the larger number of images in the training set.

Without loss off generality, each radiologist who performed the annotations can use an arbitrary number of tissue types.

FIG.22illustrates an exemplary application that used to demonstrate aspects of the present invention, in this case being for classification of atherosclerotic plaque phenotype classification (using specific subject data by way of example). Different colors represent different tissue analyte types with dark gray showing the otherwise normal wall. The figure illustrates the result of ground truth annotation of tissue characteristics that are indicative of plaque phenotype as well as the spatial context of how they present in a cross section taken orthogonal to the axis of the vessel. It also illustrates a coordinate system that has been developed in order to provide a common basis for analysis of a large number of histological cross sections. Grid lines added to demonstrate coordinate system (tangential vs. radial distance) and overlaid on top of color-coded pathologist annotations. An important aspect of this is that data sets of this kind may be used efficiently in deep learning approaches because they simplify the information using a relatively simpler false color image in place of a higher-resolution full image but without losing spatial context, e.g., to have a formal representation for such presentations as “napkin ring sign”, juxtaluminal calcium, thin (or thick) caps (spacing between LRNC and lumen), etc.

FIG.23illustrates tangential and radial direction variable internally represented using unit phasors and here, phasor angle shown coded in gray scale, which exemplifies the use of normalized axes for tubular structures relevant to the vascular and other pathophysiology associated with such structures (e.g., the gastro-intestinal tract). Note that the vertical bar from black to white is a purely arbitrary boundary due to the gray scale encoding, and the normalized radial distance has a value of 0 at the luminal boundary and value of 1 at the outer boundary.

FIG.24illustrates an exemplary overlay of radiology analysis application generated annotations from CTA (unfilled color contours) on top of pathologist generated annotations from histology (solid color regions). An example aspect of the systems and methods presented herein is that the contours form in vivo non-invasive imaging can be used with the classification scheme so as to determine phenotype non-invasively, where the classifier is trained on known ground truth. Specifically, filling in the contours which are shown unfilled in this figure (so as to not obscure the relationship with the ex vivo annotation for this specific section which is provided to show the correspondence) creates input data for the classifier.

FIG.25demonstrates a further step of data enrichment, specifically, utilizing the normalized coordinate system to avoid non-relevant variation associated with the wall thickness and radial presentation. Specifically, the “donut” is “unwrapped” while retaining the pathologist annotations. The left panel illustrates pathologist region annotations of a histological slice of a plaque after morphing to convert the cut open “C” shape back into the in vivo “O” shape of the intact vessel wall. Horizontal axis is the tangential direction around the wall. Vertical axis is the normalized radial direction (bottom is luminal surface, top is outer surface). Also note that the finer granularity of the pathologist annotations has been collapsed to match the granularity intended for extraction by the in vivo radiology analysis application (e.g., LRNC, CALC, IPH). The right panel illustrates the comparable unwrapped radiology analysis application annotations. Axes and colors are the same as the pathologist annotations.

FIG.26represents the next refinement relevant to the plaque phenotyping example. Working from the unwrapped formalism, luminal irregularity (as results, for example from ulceration or thrombus) and local varying wall thickening are represented. The light grey at bottom represents the lumen (added in this step so as to represent that luminal surface irregularity) and the black used in the prior step is now replaced with dark grey, to represent the varying wall thickening. Black now represents area outside of the wall entirely.

FIG.27represents an additional example, so as to include, for example, intra-plaque hemorrhage and/or other morphology aspects as needed (using specific subject data by way of example). The left panel shows the donut representation and the right panel the unwrapped with the luminal surface and localized wall thickening represented.

Example CNNs tested included CNNs based on AlexNet and Inception frameworks.

AlexNet Results:

In example embodiments tested, the convolutional filter values were initialized with weights taken from AlexNet trained on the ImageNet dataset. While the ImageNet dataset is a natural image dataset, this simply serves as an effective method of weight initialization. Once training begins, all weights are adjusted to better fit the new task.

Most of the training schedule was taken directly from the open source AlexNet implementation, but some adjustment was needed. Specifically, the base learning rate was reduced to 0.001 (solver.prototxt) and the batch size was reduced to 32 (train_val.prototxt) for both the alexnet-donut and alexnet-unwrapped networks.

All models were trained to 10,000 iterations and were compared to snapshots when trained till just 2,000 iterations. While a more in depth study on overfitting could be done, it was generally found that both training and validation error decreased between 2 k and 10 k iterations.

A brand new AlexNet network model was trained from scratch for 4 (four) different combinations of ground-truth results of two leading pathologists, two different ways of processing images (see above), as well as unwrapped images and donut images. The results are listed inFIG.28. Each dataset variation had its training data augmented by 15× with class normalization enabled. A network was trained on this augmented data and then was tested on the corresponding un-augmented validation data corresponding to that variation. For the unwrapped data an AlexNet style network with 400×200 pixel input was used, and the donut network is AlexNet style with 280×280 pixel input (roughly the same resolution but different aspect ratio). Note that in test embodiments the dimensions of the conventional layers as well as the fully connected layers were changed. Thus, the network in the AlexNet test embodiments can be described as a five convolutional layer, three fully connected layer network. Without loss of generality, here are some high-level conclusions that are illustrated from these results:1) With the exception of the WN_RV dataset, it does indeed seem to be that the unwrapped data is easier for the data to analyze as it receives higher validation accuracy across the board2) The non-normalized data is demonstrated to be more representative as anticipated.3) In regards to the WN_RV dataset, the original idea was to pool WN and RV truth data to see the compatibility of the typing systems and the degree to which sets may be merged. In doing so, significant differences were observed in the WN vs RV data. The original intention of the WN_RV experiments was to pool training data from multiple pathologists to see if the information contributed to efficacy. Instead degradation rather than improvement was observed. This was determined to be because of variations in color scheme which impeded such pooling of data. Thus, one can consider normalizing the color scheme to enable pooling.
Exemplary Alternative Network: Inception:

Transfer-learning re-training of an Inception v3 CNN was started with the Aug. 8 2016 version of the network uploaded on the TensorFlow site for public use. The network was trained for 10,000 steps. Training and Validation sets were normalized in number of images via image augmentation so both sub-sets amounted to the same number of annotated images. All other network parameters were taken to be at their default values.

Pre-trained CNNs can be used to classify imaging features using the output from the last convolution layer, which is a numeric tensor with dimensionality of 2048×2 in the case of the Google Inception v3 CNN. We then train an SVM classifier to recognize the object. This process is normally performed on the Inception model after a transfer-learning and Fine-Tuning steps in which the model initially trained on the ImageNet2014dataset has its last, softmax layer removed and re-trained to recognize the new categories of images.

Alternative Embodiments

FIG.29provides an alternative example, for phenotyping potentially cancerous lung lesions. The left-most panel indicates the outlines of a segmented lesion, with pre-processing to separate out into solid vs. semi-solid (“ground glass) sub regions. The middle panels indicate its location in the lung, and the right-most panel shows it with false color overlay. In this case, the 3-dimensional nature of the lesion is likely considered significant, so instead of processing 2-D cross-sections separately, techniques such as video interpretation from computer vision may be applied for the classifier input data set. In fact, processing multiple cross-sections sequentially, as if in a “movie” sequence along a centerline, can generalize these methods for tubular structures.

Another generalization is where the false colors are not selected from a discrete palette but instead have continuous values at pixel or voxel locations. Using the lung example, FIG. shows a set of features, sometimes described as so-called “radiomics” features that can be calculated for each voxel. Such a set of values may exist in arbitrary number of pre-processed overlays and be fed into the phenotype classifier.

Other alternative embodiments include using change data, for example as collected from multiple timepoints, rather than (only) data from a single timepoint. For example, if the amount or nature of a negative cell type increased, it may be said to be a “progressor” phenotype, vs. a “regressor” phenotype for decreases. The regressor might be, for example, due to response to a drug. Alternatively, if the rate of change for, say, LRNC is rapid, this may imply a different phenotype. The extension of the example to use delta values or rates of change is obvious to one skilled in the art.

As an additional alternative embodiment, non-spatial information, such as which are derived from other assays (e.g., lab results), or demographics/risk factors, or other measurements taken from the radiological image, may be fed into the final layers of the CNN to combine the spatial information with non-spatial information. Also, localized information such as use of a pressure wire with readings at one or more certain locations along a vessel from a reference such as a bifurcation or ostia, by inference of full 3D coordinates at imaging may be determined.

Whereas the focus of these examples has been on phenotype classification, similar approaches may be applied to the problem of outcome prediction, as a further embodiment of this invention.

Example Implementations

Systems and methods of the present disclosure may advantageously comprise a pipeline consisting of multiple stages.FIG.34provides a further example implementation of a hierarchical analytics framework. Biological properties are identified and quantified by semantic segmentation to identify biological properties singly or in combination, in the example application lipid-rich necrotic core, cap thickness, stenosis, dilation, remodeling ratio, tortuosity (e.g., entrance and exit angles), calcification, IPH, and/or ulceration, which is represented numerically as well as in enriched data sets with spatial unwrapping to convert the vein/artery cross-section into a rectangle, and then medical conditions (e.g., ischemia-causing fractional flow reserve FFR, high-risk phenotype HRP, and/or risk stratification time to event (TTE) using trained CNN(s) to read the enriched data sets to identify and characterize the condition. Images are collected of the patient, the raw slice data is used in a set of algorithms to measure biological properties that may be objectively validated, these are in turn formed as enriched data sets to feed one of more CNNs, in this example where results are forward and back-propagated using recurrent CNNs to implement constraints or creates continuous conditions (such as a monotonically decreasing fractional flow reserve from proximal to distal throughout the vessel tree, or constant HRP value in a focal lesion, or other constraints). Ground truth data for HRP may exist as expert pathologist determined plaque types at given cross-sections, having been determined ex vivo. Ground truth data for FFR may be from physical pressure wire, with one or more measured values, and network training for locations along the centerline proximal of a given measurement being constrained to be greater than or equal to the measured value, locations distal being constrained to be less than or equal, and when two measurements on the same centerline are known, that the values between the two measured values be constrained within the interval.

These properties and/or conditions may be assessed at a given point in time and/or change across time (longitudinal). Without loss of generality, other embodiments performing similar steps, either in plaque phenotyping or in other applications, would be embodiments of the invention.

In example implementations, biological properties can include one or more of the following:AngiogenesisNeovascularizationInflammationCalcificationLipid-depositsNecrosisHemorrhageUlcerationRigidityDensityStenosisDilationRemodeling RatioTortuosityFlow (e.g., of blood in channel)Pressure (e.g., of blood in channel or one tissue pressing against another)Cell types (e.g., macrophages)Cell alignment (e.g., of smooth muscle cells)Shear stress (e.g., of blood in channel)

Analysis can include determining one or more of quantity, degree and/or character for each of the aforementioned biological properties.

Conditions that can be determined based on the biological properties may include one or more of:Perfusion/ischemia (as limited) (e.g., of brain or heart tissue)Perfusion/infarction (as cut off) (e.g., of brain or heart tissue)OxygenationMetabolismFlow reserve (ability to perfuse) e.g., FFR(+) vs. (−) and/or continuous numberMalignancyEncroachmentHigh-risk plaque e.g., HRP(+) vs. (−) and/or labelled phenotypeRisk stratification (whether as probability of event, or time to event) (e.g., MACCE, mentioned explicitly)

Validation in the form of truth bases can include the following:BiopsyExpert tissue annotations form excised tissue (e.g., endarterectomy or autopsy)Expert phenotype annotations on excised tissue (e.g., endarterectomy or autopsy)Physical pressure wireOther imaging modalitiesPhysiological monitoring (e.g., ECG, SaO2, etc.)Genomic and/or proteomic and/or metabolomics and/or transcriptomic assayClinical outcomes

Analysis can be both at a given point in time as well as longitudinal (i.e., change across time)

Exemplary System Architecture:

FIG.31illustrates a high-level view of the users and other systems that interact with an analytics platform, as per the systems and methods of the present disclosure. Stakeholders of this view include System Administrators, Support Technicians, which have Interoperability, Security, Failover & Disaster Recovery, Regulatory concerns.

The platform can be deployed in two main configurations; on-premises, or remote server (FIG.32). The platform deployment may be a stand-alone configuration (Left Upper), on-premises server configuration (Left Lower), or remote server configuration (Right). The on-premises deployment configuration can have two sub-configurations; desktop only or rackmount. In the remote configuration, the platform may be deployed on a HIPAA compliant data center. Clients access that API server over a secure HTTP connection. Clients can be desktop or tablet browsers. No hardware except for the computers running the web browsers is deployed on the customer site. The deployed server may be on a public cloud or an extension of the customer's private network using a VPN.

An exemplary embodiment is comprised of a client and a server. For example,FIG.33illustrates a client as a C++ application and the server as a Python application. These components interact using HTML 5.0, CSS 5.0 and JavaScript. Wherever possible open standards are used for interfaces including but not limited to; HTTP(S), REST, DICOM, SPARQL, and JSON. Third party libraries are also used as shown in this view which shows the primary pieces of the technology stack. Many variations and different approaches may be understood by people skilled in the art.

Various embodiments of the above-described systems and methods may be implemented in digital electronic circuitry, in computer hardware, firmware, and/or software. The implementation can be as a computer program product (i.e., a computer program tangibly embodied in an information carrier). The implementation can, for example, be in a machine-readable storage device and/or in a propagated signal, for execution by, or to control the operation of, data processing apparatus. The implementation can, for example, be a programmable processor, a computer, and/or multiple computers.

A computer program can be written in any form of programming language, including compiled and/or interpreted languages, and the computer program can be deployed in any form, including as a stand-alone program or as a subroutine, element, and/or other unit suitable for use in a computing environment. A computer program can be deployed to be executed on one computer or on multiple computers at one site.

Method steps can be performed by one or more programmable processors executing a computer program to perform functions of the invention by operating on input data and generating output. Method steps can also be performed by and an apparatus can be implemented as special purpose logic circuitry. The circuitry can, for example, be a FPGA (field programmable gate array) and/or an ASIC (application specific integrated circuit). Modules, subroutines, and software agents can refer to portions of the computer program, the processor, the special circuitry, software, and/or hardware that implements that functionality.

Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor receives instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a processor for executing instructions and one or more memory devices for storing instructions and data. Generally, a computer can include, can be operatively coupled to receive data from and/or transfer data to one or more mass storage devices for storing data (e.g., magnetic, magneto-optical disks, or optical disks).

Data transmission and instructions can also occur over a communications network. Information carriers suitable for embodying computer program instructions and data include all forms of non-volatile memory, including by way of example semiconductor memory devices. The information carriers can, for example, be EPROM, EEPROM, flash memory devices, magnetic disks, internal hard disks, removable disks, magneto-optical disks, CD-ROM, and/or DVD-ROM disks. The processor and the memory can be supplemented by, and/or incorporated in special purpose logic circuitry.

To provide for interaction with a user, the above described techniques can be implemented on a computer having a display device. The display device can, for example, be a cathode ray tube (CRT) and/or a liquid crystal display (LCD) monitor. The interaction with a user can, for example, be a display of information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer (e.g., interact with a user interface element). Other kinds of devices can be used to provide for interaction with a user. Other devices can, for example, be feedback provided to the user in any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback). Input from the user can, for example, be received in any form, including acoustic, speech, and/or tactile input.

The above described techniques can be implemented in a distributed computing system that includes a back-end component. The back-end component can, for example, be a data server, a middleware component, and/or an application server. The above described techniques can be implemented in a distributing computing system that includes a front-end component. The front-end component can, for example, be a client computer having a graphical user interface, a Web browser through which a user can interact with an example implementation, and/or other graphical user interfaces for a transmitting device. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), the Internet, wired networks, and/or wireless networks.

The system can include clients and servers. A client and a server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

Packet-based networks can include, for example, the Internet, a carrier internet protocol (IP) network (e.g., local area network (LAN), wide area network (WAN), campus area network (CAN), metropolitan area network (MAN), home area network (HAN)), a private IP network, an IP private branch exchange (IPBX), a wireless network (e.g., radio access network (RAN), 802.11 network, 802.16 network, general packet radio service (GPRS) network, HiperLAN), and/or other packet-based networks. Circuit-based networks can include, for example, the public switched telephone network (PSTN), a private branch exchange (PBX), a wireless network (e.g., RAN, Bluetooth, code-division multiple access (CDMA) network, time division multiple access (TDMA) network, global system for mobile communications (GSM) network), and/or other circuit-based networks.

The computing device can include, for example, a computer, a computer with a browser device, a telephone, an IP phone, a mobile device (e.g., cellular phone, personal digital assistant (PDA) device, laptop computer, electronic mail device), and/or other communication devices. The browser device includes, for example, a computer (e.g., desktop computer, laptop computer) with a World Wide Web browser (e.g., Microsoft® Internet Explorer® available from Microsoft Corporation, Mozilla® Firefox available from Mozilla Corporation). The mobile computing device includes, for example, a Blackberry®, iPAD®, iPhone® or other smartphone device.

Whereas many alterations and modifications of the disclosure will no doubt become apparent to a person of ordinary skill in the art after having read the foregoing description, it is to be understood that the particular embodiments shown and described by way of illustration are in no way intended to be considered limiting. Further, the subject matter has been described with reference to particular embodiments, but variations within the spirit and scope of the disclosure will occur to those skilled in the art. It is noted that the foregoing examples have been provided merely for the purpose of explanation and are in no way to be construed as limiting of the present disclosure.

Although the present disclosure has been described herein with reference to particular embodiments, the present disclosure is not intended to be limited to the particulars disclosed herein; rather, the present disclosure extends to all variations and generalizations thereof that would be apparent to a person of ordinary skill in the art including those within the broadest scope of the appended claims.