Patent Description:
Examples described herein relate to a method of determining a disease state of tissue with quantitative images and a system for performing same.

Medical images are comprised of intensity values at various points in space which are typically arranged in a regular cartesian grid. The grid may for example be two-dimensional, such as for planar x-ray imaging or MR slice imaging; three-dimensional, such as for 3D CT or 3D MRI imaging, or four dimensional, such as for respiratory-correlated 4D CT imaging. Intensity values depend on the imaging modality being used; for example, they may correspond to x-ray attenuation, or be related to a concentration of water molecules. Intensity values are often relative, e.g., their values do not have any meaning on their own but only in providing contrast between neighboring points in the grid. Clinicians may, for example, use the variations in contrast to visually distinguish between cancerous and non-cancerous tissues.

In quantitative medical imaging, acquisition and reconstruction techniques attempt to produce images whose intensity values are absolute, e.g., their intensity values are related to a physical or functional property of the patient at that point. A potential advantage of quantitative imaging is that they could be used to diagnose areas of malignancy within the patient based on intensity values themselves, rather than subjectively comparing variations in intensity across the images. In practice, quantitative medical imaging is confounded by large uncertainties in the relationship between intensity values and patient properties.

Visually searching through large medical imaging datasets and interpreting intensity values in light of these variations is both time consuming and requires a high degree of experience, particularly for whole body imaging where there is no known limited region of suspected disease. Methods to automate diagnosis are confounded by the uncertainties in intensity values. The present disclosure is aimed at meeting such a need. <CIT> relates to systems, methods, and computer program products for analysis of vessel attributes for diagnosis, disease staging, and surgical planning. A method for analyzing blood vessel attributes may include developing an atlas including statistical measures for at least one blood vessel attribute. The statistical measures can be developed from blood vessel image data from different individuals. Blood vessel attribute measurements can be obtained from an individual subject. The individual subject's blood vessel attribute measurements can be compared to the statistical measures in the atlas. Output may be produced indicative of a physical characteristic of the individual based on results from the comparison. <CIT> relates to methods and systems for synchronizing a view of a patient image with an atlas image. Certain embodiments provide a method for synchronizing a patient image with an atlas image. The method includes retrieving an image atlas including at least one atlas image, registering an atlas image to a patient image and synchronizing a view of the atlas image to a view of the patient image. In certain embodiments, the method further includes registering a plurality of atlas images to a plurality of patient images. In certain embodiments, the step of synchronizing further includes synchronizing at least one of orientation, zoom level, window level and pan of the atlas image to the patient image. <CIT> relates to a system and method of computer aided analysis of medical images and detection of malignant lesions is described. Medical images obtained from multiple modalities are analyzed. Morphological features as well as temporal, i.e., kinetics features, are combined to compute a consolidated assessment of a possible lesion detected in the medical images. The system includes at least one kinetics module, which is capable of extracting kinetics features from a time sequence of MRI images or MRS data taken after administering a contrast enhancement agent to a patient. The consolidated assessment is presented to a user for confirmation or modification. <CIT> relates to a phantom calibration body for calibrating diffusion MRI device that mimics a material such as a mammalian tissue. The phantom calibration body includes a homogeneous aqueous solution that contains a mixture of low molecular-weight and high molecular-weight polymers housed in a container that is placed in the diffusion MRI device for obtaining one or more diffusion MRI images of the phantom calibration body. A measure of diffusivity is calculated for each of the one or more diffusion MRI images in order to calibrate the diffusion MRI device.

The present invention is defined as in the appended claims. Example methods and systems for determining candidate elements and/or disease states of same are disclosed herein. An example method may include providing at least one quantitative image of a subject, each of the at least one quantitative images including elements, providing an atlas corresponding to the subject, the atlas including atlas elements, each of the atlas elements comprising a metric distribution, determining a correspondence map relating elements of the at least one quantitative image to corresponding atlas elements, and determining candidate elements by comparing a value of certain elements of the at least one quantitative image with the metric distribution of related atlas elements of the correspondence map.

The example method may also include providing at least one additional image of the subject, each of the at least one additional images including elements, localizing the candidate elements to corresponding elements of the at least one additional image, classifying the candidate elements into at least one class based on properties of the corresponding elements of the at least one additional image, and determining a disease state of the candidate elements by analyzing the at least one class of the at least one additional image corresponding to the candidate elements.

An example method may include localizing candidate elements of a quantitative image to corresponding elements of at least one additional image, classifying the candidate elements into at least one class based on properties of the corresponding elements of the at least one additional image, and determining a disease state of the candidate elements by analyzing the at least one class of the at least one additional image corresponding to the candidate elements.

An example system may include at least one processing unit and at least one computer readable media encoded with instructions which, when executed cause the system to: provide at least one quantitative image and at least one additional image of a subject, each of the at least one quantitative image and the at least one additional image including elements; provide an atlas corresponding to the subject, the atlas including atlas elements, each of the atlas elements including a metric distribution; determine a correspondence map relating elements of the at least one quantitative image to corresponding atlas elements; determine candidate elements by comparing a value of certain elements of the at least one quantitative image with the metric distribution of related atlas elements of the correspondence map; localize the candidate elements to corresponding elements of the at least one additional image; classify the candidate elements into at least one class based on properties of the corresponding elements of the at least one additional image; and determine a disease state of the candidate elements by analyzing the at least one class of the at least one additional image corresponding to the candidate elements.

The foregoing and other features of the present disclosure will become more fully apparent from the following description and appended claims, taken in conjunction with the accompanying drawings. Understanding that these drawings depict only several examples in accordance with the disclosure and are, therefore, not to be considered limiting of its scope, the disclosure will be described with additional specificity and detail through use of the accompanying drawings, in which:.

In the following detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, similar symbols typically identify similar components, unless context dictates otherwise. The illustrative examples described in the detailed description and the drawings are not meant to be limiting. Other examples may be utilized, and other changes may be made, without departing from the scope of the subject matter presented herein.

This disclosure is drawn, inter alia, to methods, systems, products, devices, and/or apparatuses generally related to providing at least one quantitative image of a subject, each of the at least one quantitative images including elements. An atlas corresponding to the subject may be provided. The atlas includes atlas elements, each of the atlas elements including a metric distribution. A correspondence map relating elements of the at least one quantitative image to corresponding atlas elements may be determined. Candidate elements may be determined by comparing a value of certain elements of the at least one quantitative image with the metric distribution of related atlas elements of the correspondence map. The disclosure may also involve providing at least one additional image of the subject, each of the at least one additional images including elements. The candidate elements may be localized to corresponding of the elements of the at least one additional image. The candidate elements may be classified into at least one class based on properties of the corresponding elements of the at least one additional image. A disease state of the candidate elements may be determined by analyzing the at least one class of the at least one additional image corresponding to the candidate elements.

<FIG> is a flow chart of a method for determining a disease state of tissue arranged in accordance with an embodiment of the present disclosure. An example method may include one or more operations, functions or actions as illustrated by one or more of blocks <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, and/or <NUM>. The operations described in the blocks <NUM> through <NUM> may be performed in response to execution (such as by one or more processors described herein) of computer-executable instructions stored in a computer-readable medium, such as a computer-readable medium of a computing device or some other controller similarly configured.

The method <NUM> includes at least one image <NUM>. Block <NUM> depicts "Localization of candidate elements" and may accept the at least one image <NUM> as an input. Block <NUM> may output candidate elements <NUM>. Block <NUM> may be followed by block <NUM>, which may accept the candidate elements <NUM> as inputs and which depicts "Filtering of candidate elements". Block <NUM> may output a diseased element <NUM> of tissue.

The one or more images <NUM> may be medical images of a subject. Images <NUM> may be acquired during method <NUM>, or may have been previously acquired. If previously acquired, the images <NUM> may be stored on storage media, such as computer readable media until ready for use in method <NUM>. The images <NUM> may have been acquired at the same location the method <NUM> is being performed, or may have been transmitted from a remote location. The images <NUM> may be transmitted via a wired or wireless connection to location the method <NUM> is being performed. Images <NUM> may be a single image or a plurality of images, which may include a mix of different imaging modalities. The modalities may include magnetic resonance imaging (MRI), ultrasound, optical imaging, x-ray, computed tomography (CT), positron emission tomography (PET), or combinations thereof. The images <NUM> may be quantitative images, non-quantitative images, or a combination. The at least one image <NUM> may include elements such as pixels, voxels, groups of voxels corresponding to an anatomical structure, surfaces, or combinations thereof.

Block <NUM> recites "Localization of candidate elements". The at least one image <NUM> is analyzed for abnormal regions. Properties of each of the elements (or groups of elements) of the image <NUM> are determined and analyzed. The elements may be components of the image, and may be raw image data (e.g., intensity values for each pixel), may be processed image data (e.g., a normalized or background corrected intensity values for each pixel), may be groupings of image components into identified features (e.g., coordinates of a centroid of pixels identified as a tissue structure), or may be combinations thereof. Multiple different types of elements may exist in images <NUM>. Different properties and values may be associated with different types of elements (e.g., location, intensity, texture, type of tissue, etc.). In some examples the elements may be pixels, voxels, groups of voxels that have been identified as anatomical structures, surfaces that have been extracted from the data via a segmentation algorithm, annotations, points in image coordinate space, or combinations. Other elements maybe used in other examples. The elements may be compared to a normal range or probability distributions of corresponding to different tissues. Elements determined to be of interest are output as candidate elements <NUM>. The candidate elements <NUM> may represent a subset of the elements of the image <NUM>. The candidate elements <NUM> may be regions with abnormal tissue properties and may be regions of interest for further clinical investigation. The candidate elements <NUM> may be used on their own, for example, to guide medical treatment of a subject. The method <NUM> may continue with block <NUM> which filters the candidate elements <NUM>.

Block <NUM> recites "Filtering of candidate elements". Block <NUM> may accept the candidate elements <NUM> as an input. Block <NUM> may use additional properties of the at least one image <NUM> to determine which (if any) of the candidate elements <NUM> represent diseased tissue. Block <NUM> may use the same or different information from the at least one image <NUM> as block <NUM>. Block <NUM> and Block <NUM> may use different images which correspond to the same patient. Block <NUM> may output a set of diseased elements <NUM>, which represent a subset of the candidate elements <NUM>. The diseased elements <NUM> may represent areas of the images which correspond to diseased tissue of the subject.

Although the method <NUM> shows blocks <NUM> and <NUM> functioning together, it is to be understood that each could be used independently. For example, a clinician could determine candidate regions of interest through some other method and then use those as an input to block <NUM> for filtering. Similarly, a clinician could analyze a set of images with block <NUM> and then use the produced candidate elements <NUM> without proceeding to block <NUM>.

The method <NUM> may involve feedback from an operator (e.g., a doctor or technician) or may operate automatically in a user-independent fashion. The candidate elements <NUM> may be presented to the operator for review. The operator may be asked to accept the located candidate elements <NUM> or edit them before block <NUM>. The method <NUM> may display the candidate elements <NUM> and/or the diseased elements <NUM> as results to an operator. The operator may review, validate, and/or edit the results. The operator may manage patient health based on the results. As an example, further tests may be ordered based on the candidate elements <NUM>, or treatment managed based on the status of the diseased elements <NUM>. The method <NUM> may be used to automatically manage patient health by using the candidate elements <NUM> and/or disease elements <NUM> with an automated health management system, such as a dosage calculator. Accordingly, identification of one or more diseased elements may result in a diagnosis - e.g., cancer, inflammation, tumor. Responsive to the diagnosis, various pharmaceuticals may be administered to the patient, and/or treatments preformed (e.g., biopsy, chemotherapy, dialysis, etc.).

<FIG> is a schematic diagram depicting a system for determining a disease state of tissue. The system <NUM> may be used to perform a method of determining a disease state of tissue, such as method <NUM> of <FIG>. The system <NUM> includes a subject (e.g., a patient) <NUM>, candidate locations (or regions) <NUM>', disease locations (or regions) <NUM>', imaging unit <NUM>, and operations unit <NUM>. The operations unit <NUM> may include an input/output unit (e.g., a display) <NUM>, a controller <NUM>, a processor <NUM>, and a memory <NUM>. The imaging unit <NUM> may produce an image <NUM> which may be sent to the operations unit <NUM> to be stored in memory <NUM> and/or displayed on input/output unit <NUM>. The image <NUM> includes candidate elements <NUM> and disease elements <NUM>. The memory <NUM> may include instructions <NUM>, including instructions to localize a candidate element <NUM> and filter the candidate elements <NUM>, and an atlas <NUM>.

The subject <NUM> may include candidate regions <NUM>' and/or disease locations <NUM>' within their body. The imaging unit <NUM> may scan or image all or part of the subject <NUM> to produce data which is sent to operations unit <NUM> to form an image <NUM>. The operations unit <NUM> may include an input/output unit <NUM>, a controller <NUM>, a processor <NUM>, and memory <NUM>. The memory stores instructions <NUM> which may be accessed to cause the processor <NUM> to perform certain operations. The processor <NUM> and/or memory may receive data from the imaging unit <NUM> to produce image <NUM>. The processor <NUM> may execute instructions <NUM> of the memory such as localizing candidate elements <NUM> and/or filtering candidate elements <NUM> to produce candidate elements <NUM> and disease elements <NUM> of the image <NUM>. The candidate elements <NUM> of the image <NUM> may correspond to the candidate locations <NUM>' of the subject <NUM>, and the disease elements <NUM> of the image <NUM> may correspond to the disease locations <NUM>' of the subject <NUM>. The atlas <NUM> may be accessed by the processor <NUM> while executing one or more the instructions <NUM>.

The subject <NUM> may be a mammal such as a human. The subject <NUM> may be a patient who is exhibiting symptoms or is undergoing treatment and/or monitoring for a disease or condition. The subject <NUM> may be a member of the general population or otherwise asymptomatic. The subject <NUM> may have one or more candidate locations <NUM>' and/or one or more diseased region <NUM>'. The regions <NUM>', <NUM>' may be on an external surface of the subject <NUM> (e.g., on the skin) or may be located internally. The one or more diseased regions <NUM>' may be a subset of the one or more candidate locations <NUM>'. The candidate locations <NUM>' may be areas of tissue which have properties which fall outside, or have a sufficient probability of falling outside, a normal clinical range for that tissue. They may also be detected using an artificial intelligence algorithm. The one or more diseased regions <NUM>' may be locations containing structures which are harmful to the subject <NUM> or which may become harmful to the subject <NUM> in the future. In one example, the diseased region <NUM>' may be a region of cancerous cells, such as a tumor. Other diseased tissues or conditions may be investigated in other examples.

The imaging unit <NUM> may produce one or more images (such as images <NUM>) of the subject <NUM>. The imaging unit may produce a single image or a plurality of images of the subject <NUM>. The imaging unit <NUM> may produce images of all or a portion of the subject <NUM>. The imaging unit <NUM> may include multiple imaging modalities such as MRI, ultrasound, optical imaging, x-ray, CT, PET, or combinations thereof. In some cases the multiple imaging modalities may physically be installed in different locations, and the patient transported to the different locations for separate imaging sessions. The imaging unit <NUM> may produce a whole body scan of the subject <NUM>. The imaging unit may produce quantitative images, non-quantitative images, or combinations. The imaging unit <NUM> may be coupled to the operations unit <NUM> by wired and/or wireless communication. The imaging unit <NUM> may send raw data to the operations unit <NUM> or may send processed data to the operations unit <NUM>. The imaging unit <NUM> and the operations <NUM> may be located remotely from each other. The data may be stored on a non-transitory medium, such as a computer readable medium by the imaging unit <NUM>, and retrieved by the operations unit <NUM> at a later time.

The images <NUM> produced by the imaging unit <NUM> may be used as images <NUM> of <FIG>. The images <NUM> may be stored, displayed, and/or analyzed by the operations unit <NUM>. The images <NUM> may be representative of the subject <NUM>. The images may include elements such as pixels, voxels, groups of voxels corresponding to an anatomical structure, surfaces, or combinations thereof. The images <NUM> may include quantitative images, where a value (e.g., an intensity) of each element of the image <NUM> has an absolute value which relates to a property of the subject <NUM> at a location corresponding to the element. The images may undergo image processing by the processor <NUM> and/or by the imaging unit <NUM>. The image processing may include correcting for distortions or artifacts, normalization of values, or combinations. The values of the elements of the images <NUM> may be normalized so that they are, for example, between <NUM> to <NUM>. Other ranges may be used in other examples. The imaging unit <NUM> may be calibrated. The calibration may involve using the imaging unit <NUM> to produce an image of a tissue phantom (not shown) with known properties.

The operations unit <NUM> receives data from the imaging unit <NUM>. The operations unit <NUM> may be a computer. The operations unit <NUM> uses data from the imaging unit <NUM> to produce the images <NUM>. The data may be saved into memory <NUM>. Controller <NUM> may cause the processor <NUM> to render the images <NUM> onto an input/output <NUM>. In some examples, the input/output <NUM> may be a display, such as a computer monitor. The controller <NUM> may also send instructions to imaging unit <NUM> to control aspects of the imaging of the subject <NUM>. The controller <NUM> may cause the processor <NUM> to execute instructions <NUM> stored in the memory.

The instructions <NUM> in memory <NUM> may be used to analyze the images <NUM>. The instructions <NUM> may include instructions to localize candidate elements <NUM> (such as block <NUM> of <FIG>). The instructions <NUM> may include instructions to filter candidate elements <NUM> (such as block <NUM> of <FIG>). The instructions <NUM>, <NUM> may be run sequentially on the same images <NUM> or may be run as separate steps. The instructions <NUM>, <NUM> may produce candidate elements <NUM> and/or diseased elements <NUM> of the images <NUM>. The input/output <NUM> may display the images <NUM> along with one or more of the candidate elements <NUM> and diseased elements <NUM>. The memory <NUM> may store additional information such as atlas <NUM>, which may be used in one or more of the instructions <NUM>.

<FIG> is a flow chart of a method <NUM> of localizing candidate elements arranged in accordance with an embodiment of the claimed invention. An example method may include one or more operations, functions or actions as illustrated by one or more of blocks <NUM>, 302a, 302b, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, and/or <NUM>. The operations described in the blocks <NUM> to <NUM> may be performed in response to execution (such as by one or more processors described herein) of computer-executable instructions stored in a computer-readable medium, such as a computer-readable medium of a computing device or some other controller similarly configured.

The method <NUM> includes at least one image <NUM> including quantitative images 302a. The images <NUM> also include additional images 302b, which may be non-quantitative images or a combination of quantitative images and non-quantitative images. The method also includes atlas <NUM> which includes atlas elements <NUM> and metric distributions <NUM>. An example process may begin with block <NUM>, which recites "Determine correspondence map" and accepts the images <NUM> and the atlas <NUM> as inputs. Block <NUM> may be followed by block <NUM> which recites "Assign metric distributions to quantitative image elements. " Block <NUM> may be followed by block <NUM>, which recites "Localize candidate elements". Block <NUM> may be followed by block <NUM>, which recites "Store candidate elements or filter".

The method <NUM> may be a detailed method of localizing candidate elements, such as methods <NUM> and <NUM> of <FIG>. The images <NUM> may be similar to, or may be the same as, images <NUM> of <FIG> and images <NUM> of <FIG>. The images <NUM> include a mix of quantitative images 302a and additional images 302b. The quantitative images 302a and additional images 302b are each composed of elements as described herein. The quantitative images 302a have elements with values which are absolute, e.g., their values are related to a physical or functional property at that point of the image. The quantitative images 302a may be calibrated. The quantitative images 302a include quantitative MRI, quantitative PET, quantitative ultrasound, or combinations. Examples of quantitative images include a whole body diffusion weighted imaging MRI (DWI MRI). Examples of non-quantitative images include short-TI inversion recovery (STIR) MRI, T1-weighted MRI images, T2-weighted MRI images, CT, PET, single-photon emission computed tomography (SPECT), ultrasound, or combinations.

The atlas <NUM> is a representation of average values from a normal patient population. The atlas <NUM> may be constructed from one or more previously acquired images from a normal patient population, may be artificially generated from statistics or generalizations about a normal patient population, or combinations. The atlas <NUM> may include a single composite image of an average or representative patient, or a number of such images. The atlas <NUM> may represent all normal patients, or may represent a subset of the normal patient population. The subset may be restricted by, for example, one or more of gender, age, weight, height, body shape, or other factors. The atlas <NUM> may be chosen or filtered to correspond to a subject of the images <NUM> (such as subject <NUM> of <FIG>). In an example, if the subject is a <NUM> year old woman, the atlas might be filtered by 'female' and 'teenager' and an atlas may be produced which averages only patient scans of female teenagers. Other filters may be used in other examples.

The atlas <NUM> may include atlas elements <NUM> and metric distributions <NUM>. The atlas elements <NUM> may each be associated with one or more of the metric distributions <NUM>. The atlas elements <NUM> may be image pixels, image voxels, segmented surfaces, reference points, reference structures, or combinations. The metric distributions <NUM> may be a range of 'normal' values for each atlas element <NUM>. The metric distribution <NUM> may include intensity values or values calculated from intensity values, such as average intensity or texture value. In one embodiment where the quantitative images 202a are DWI MRI images, the metric distributions <NUM> are a range of expected diffusion values for each atlas element <NUM>. Other quantitative images and metric distributions may be used in other examples. The metric distributions <NUM> may be normalized values. The metric distributions <NUM> may be a range of probability density distribution of intensity values, an average and standard deviation of intensity values, a range of expected texture values, or combinations. The atlas <NUM> may include multiple images each including atlas elements with metric distributions.

Block <NUM> describes "Determine Correspondence Map" and accepts inputs including the quantitative images 302a and atlas <NUM>. During block <NUM>, each element of the quantitative images 302a is matched with one or more of the atlas elements <NUM>. The atlas elements <NUM> may be mapped onto elements of the quantitative image 302a, or the elements of the quantitative image 302a may be mapped onto the atlas elements <NUM>. The mapping may adjust for differences between the size and shape of the normal patient(s) in the atlas <NUM> and the size and shape of the subject of the quantitative images 302a. In one embodiment, the correspondence mapping may be performed by using a deformable registration algorithm. In another embodiment, the quantitative image 302a may be segmented to separate it into one or more structure elements, and the atlas elements <NUM> may be defined as structure elements. The quantitative image 302a elements may be matched to the atlas elements <NUM> by comparing size, morphology, and/or positioning of the elements. For example, lymph node chains may have sizes of about <NUM> in the neck and <NUM> in the chest, which can be combined with morphological information to identify corresponding structures in the images. Other methods of determining the correspondence map, such as using artificial intelligence algorithms, may be used in other examples.

<FIG> is a schematic diagram of a correspondence map which may be used as the correspondence map of block <NUM> of <FIG>. <FIG> includes an atlas <NUM> with atlas elements 426a and an image <NUM> with mapped atlas elements 426b. In the example of <FIG>, the atlas elements 426a represent structures which are being mapped onto corresponding elements of the image <NUM>. As shown by the arrows, the atlas elements 426a undergo a change in size and shape as they are mapped onto image <NUM> to become mapped elements 426b. Each set of corresponding elements 426a,b may have a metric distribution corresponding to the structure, type of tissue, or other features that are shared by the set.

Referring back to <FIG>, after determining a correspondence map, the method <NUM> continues with block <NUM> which recites "Localize candidate elements". The elements of the quantitative image 302a are compared with the metric distributions <NUM> of the corresponding atlas elements <NUM> determined by the correspondence map. The comparing may involve a direct comparison such as comparing values to a threshold, or may involve machine learning, deep learning, or other artificial intelligence algorithm. In some embodiments, a user (e.g., a clinician) may provide input to the system in order to localize the candidate elements.

<FIG> depicts a schematic diagram of metric distributions which may be an example of the localizing candidate elements of block <NUM> of <FIG>. <FIG> includes elements 536a-c which may be elements of a quantitative image, and ranges 527a-c which may be metric distributions of an atlas. In this example, comparison of the values of the elements of the quantitative images and the metric distributions of the corresponding of the atlas elements is done by determining if the value of the quantitative image element falls within the range of values of the metric distribution. <FIG> depicts three elements 536a-c, each of which is associated with a separate range of values 527a-c. Elements 536a and 536c have values which fall within the ranges 527a and 527c respectively, and would be considered normal (e.g., not a candidate element). Element 536b falls outside the range 527b, and would not be considered normal. Element 536b may be reported as a candidate element (e.g., candidate element <NUM> of <FIG>). Other methods of comparing the metric distributions to the values of the quantitative images may be used in other examples.

Referring back to <FIG>, the method <NUM> involves block <NUM>, which recites "Store candidate elements or filter". The localized candidate elements may be stored on a memory (such as memory <NUM> of <FIG>) and/or undergo further analysis, such as filtering. The candidate elements may undergo additional processing. The coordinates or a centroid of the candidate elements may be identified. The candidate elements may be grouped together into unified elements. A system (such as system <NUM> of <FIG>) may display the localized candidate elements. The candidate elements <NUM> may be displayed as an overlay on the images <NUM>.

<FIG> is a flow chart of a method <NUM> of filtering candidate elements arranged in accordance with an embodiment of the claimed invention. An example method may include one or more operations, functions or actions as illustrated by one or more of blocks <NUM>, 602a, 602b, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>. The operations described in the blocks <NUM> to <NUM> may be performed in response to execution (such as by one or more processors described herein) of computer-executable instructions stored in a computer-readable medium, such as a computer-readable medium of a computing device or some other controller similarly configured.

The method <NUM> includes at least one image <NUM> including quantitative images 602a and additional images 602b. An example process may begin with block <NUM>, which recites "Localize corresponding candidate elements in additional images" and accepts the additional images 602b, and the results of block <NUM> "localize candidate elements" as input. Block <NUM> may be followed by block <NUM> which recites "Classify corresponding candidate elements. " Block <NUM> may be followed by block <NUM>, which recites "Determine disease state of candidate elements". Block <NUM> accepts as an input block <NUM>, which recites "Logical relationship". Block <NUM> may output diseased elements <NUM>.

The method <NUM> may be used as the filtering of candidate elements <NUM>, <NUM> of <FIG> and <FIG> respectively. The method <NUM> may be a continuation of method <NUM>, or may be a standalone method. Similarly the images <NUM> including quantitative images 602a, and additional images 602b may be the images <NUM>, <NUM>, <NUM>, 302a, 302b of <FIG> respectively. Block <NUM> is shown reciting "Localize candidate elements", and may represent the method <NUM> of <FIG>, which results in a set of candidate elements. The method <NUM> may also be used on its own with new images <NUM> and a set of candidate elements determined by a different process, such as identification by a clinician.

Block <NUM> recites "Localize corresponding candidate elements in additional images". During this step, candidate elements identified in quantitative images 602a (such as by block <NUM>) are located in corresponding additional images 602b.

According to the claimed invention, the additional images include at least one non-quantitative image. The additional images 602b may be made up of elements similar to the elements of quantitative images 602a. The candidate elements may be individual elements or groups of elements which were previously identified for further review. In one example, coordinates of the candidate elements may be matched to coordinates of elements in the additional images 602b. In another example, a non-rigid registration algorithm may be used to register quantitative images 602a to the additional images 602b. In yet another example, common elements of the images 602a,b may be identified by segmenting structures in each image. Other methods of localization may be used in other examples.

Block <NUM> recites "Classify corresponding candidate elements". Properties of the additional images 602b corresponding to the candidate elements may be assigned to different classes. Classes may be binary, such as 'dark' and 'bright', or 'hard' and 'soft'. Classes may also involve more than two elements such as 'dark', 'bright', and 'undetermined' or a number of discrete values assigned (e.g., brightness on a scale from <NUM>-<NUM>). The class of a given candidate element may be determined by comparing average, standard deviations, or texture values within a candidate element to average surrounding values. The comparing may be to determine if the value within the candidate element is higher or lower than an average of surrounding elements. In some examples, a machine learning algorithm may be used to assign classes to the candidate elements.

Block <NUM> recites "Determine disease state of candidate elements". Based on the classes applied to the candidate elements, they may be assigned a status of diseased or not diseased. Other statuses, such as, for example, a type of disease may also be determined. A logical relationship <NUM> may be applied to the classes of the candidate elements from the non-quantitative images 602b. The logical relationship <NUM> may be specific to the types of images <NUM> and/or the application. The logical relationship <NUM> may reflect a logical understanding of what circumstances might have caused an element of the quantitative images 602a to be identified as a candidate element. The logical relationship <NUM> may compare classes of the additional images 602b, values of the quantitative images 602a, or both. The logical relationships <NUM> may be altered to reflect different image types, user preference, updated scientific knowledge, or other factors.

An example of a logical relationship is expressed in Table <NUM>. In this example there is a single quantitative image (DWI) and two additional non-quantitative images (STIR and T1). Candidate elements were previously located by examining the DWI image for elements with abnormal water movement. For the purposes of this example, abnormal water movement may be considered to be caused by the presence of fluid or a cyst, by blood (such as in fat or bonemarrow), or by a tumor. STIR images are bright when a fluid or cyst is present, but dark when a tumor is present. T1 images are bright when fat/bone-marrow are present, but dark when a tumor is present. The columns of Table <NUM> show the possible imaging modalities. The rows of Table <NUM> show a diagnosis based on the results of each of the <NUM> imaging modalities. In Table <NUM> an 'X' signifies that the classification of that column is unimportant to the diagnosis, and may not need to be considered. Thus, a tumor is diagnosed when a given candidate element is dark in both STIR and T1. Fluid and/or a cyst is identified when a candidate element is bright in the STIR image, and blood is identified when the T1 image is bright. Other logical relationships may be established for other examples.

In this manner, examples described herein may utilize quantitative images to identify candidate elements using a comparison with an atlas (e.g., average image values from a normal population). The candidate elements may then be located in other associated images (e.g., non-quantitative images). A binary classification (e.g., bright/dark) may be used to evaluate the candidate elements in the non-quantitative images to arrive at an identification of a diseased element.

The method <NUM> accordingly outputs diseased elements <NUM>. The diseased elements <NUM> are elements of the images <NUM> which are identified as corresponding to diseased tissue (such as disease location <NUM>' of <FIG>). The diseased elements <NUM> may reflect currently diseased tissue, or tissue which may become diseased. The method <NUM> may be altered (for example, by specifying logical relationships <NUM>) to filter for other elements of interest which are not diseased tissue. The state of each candidate element may be saved, for example, on a memory (such as memory <NUM> of <FIG>). A report may be generated to identify elements which are considered diseased by the method <NUM>.

<FIG> is a flow chart of a method <NUM> of localizing and filtering candidate elements in accordance with the present disclosure. An example method may include one or more operations, functions or actions as illustrated by one or more of blocks <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, and diseased elements <NUM>. The operations described in the blocks <NUM> to <NUM> may be performed in response to execution (such as by one or more processors described herein) of computer-executable instructions stored in a computer-readable medium, such as a computer-readable medium of a computing device or some other controller similarly configured.

The method <NUM> may include block <NUM>, which recites "Acquisition of at least one quantitative image". The method <NUM> may also include block <NUM>, "Acquisition of at least one additional image". Block <NUM> may follow block <NUM> and recites "Localization of candidate elements in quant. Block <NUM> may follow block <NUM> and block <NUM> and recites "Co-localization of candidate elements in additional images. " Block <NUM> may follow block <NUM> and recites "Classification of candidate elements in additional images". Block <NUM> may follow blocks <NUM> and <NUM> and recites "Filtering of candidate elements". Diseased elements <NUM> may be output from block <NUM>.

The method <NUM> may be similar to other methods of determining diseased elements described herein. In method <NUM>, the location of candidate elements and filtering to disease elements happen as part of a single process. The method <NUM> may be performed using a system, such as the system <NUM> of <FIG>.

Blocks <NUM> and <NUM> recite "Acquisition of at least one quantitative image" and "Acquisition of at least one additional image" respectively. The images may be acquired by an imaging unit, such as imaging unit <NUM> of <FIG>. The images may be previously acquired and may be stored until called up by method <NUM>. The images may be, for example, quantitative images 202a and additional images 202b of <FIG>. The additional images acquired in block <NUM> may be quantitative or non-quantitative. The images may correspond to the same subject. The images may include a plurality of different imaging modalities.

Block <NUM> recites "Localization of candidate elements in quant. As discussed herein, methods may be used to identify candidate elements of the quantitative images. These may include localizing the quantitative elements to an atlas and comparing elements of the quantitative image to atlas elements as described in method <NUM> of <FIG>.

Block <NUM> recites "Co-localization of candidate elements in additional images". The candidate elements determined in block <NUM> are located in the additional images acquired in block <NUM>. The localization may be generally similar to the localization of block <NUM> of <FIG>.

Block <NUM> recites "Classification of candidate elements in additional images". The candidate elements of the additional images are grouped into classes based on properties of the additional images. The classification may be generally similar to the classification of block <NUM> of <FIG>.

Block <NUM> recites "Filtering of candidate elements". In this step, the candidate elements properties in both the quantitative and additional images are compared to determine a disease state of each of the candidate elements. The value of the candidate elements in the quantitative images may be compared to one or more classes of the candidate elements in the additional images. A logical relationship may be used to determine a disease state based on the properties of the candidate elements. The output of method <NUM> may be a set of diseased elements <NUM>.

The diseased elements output by one or more of <FIG> may be used to manage a subject's health. The diseased elements may be used to diagnose a subject. For example, an estimate of survivability may be given to the subject based on the diseased elements. Followup tests may be ordered based on the diseased elements. For example, a biopsy of the subject may be performed in locations corresponding to the diseased elements. The diseased elements may be used (alone or with additional information) to direct and/or monitor a course of treatment. For example, drugs may be delivered to, or surgery may be performed at, a location of the subject corresponding to the diseased elements. Parameters of the treatment may be determined based on the diseased elements. For example, the dosage of a drug may be determined by the volume of diseased elements located in the images. The method may be repeated on the same subject over time to monitor a status of the diseased elements, such as monitoring disease progression.

The present disclosure is not to be limited in terms of the particular examples described in this application, which are intended as illustrations of various aspects. The present disclosure is to be limited only by the terms of the appended claims. It is to be understood that this disclosure is not limited to particular methods, reagents, compounds compositions or biological systems, which can, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular examples only, and is not intended to be limiting.

It will be understood by those within the art that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as "open" terms (e.g., the term "including" should be interpreted as "including but not limited to," the term "having" should be interpreted as "having at least," the term "includes" should be interpreted as "includes but is not limited to," etc.).

Furthermore, in those instances where a convention analogous to "at least one of A, B, and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B, and C" would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). In those instances where a convention analogous to "at least one of A, B, or C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B, or C" would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.).

As will be understood by one skilled in the art, for any and all purposes, such as in terms of providing a written description, all ranges disclosed herein also encompass any and all possible subranges and combinations of subranges thereof. Any listed range can be easily recognized as sufficiently describing and enabling the same range being broken down into at least equal halves, thirds, quarters, fifths, tenths, etc. As a non-limiting example, each range discussed herein can be readily broken down into a lower third, middle third and upper third, etc. As will also be understood by one skilled in the art all language such as "up to," "at least," "greater than," "less than," and the like include the number recited and refer to ranges which can be subsequently broken down into subranges as discussed above. Finally, as will be understood by one skilled in the art, a range includes each individual member. Thus, for example, a group having <NUM>-<NUM> items refers to groups having <NUM>, <NUM>, or <NUM> items. Similarly, a group having <NUM>-<NUM> items refers to groups having <NUM>, <NUM>, <NUM>, <NUM>, or <NUM> items, and so forth.

While the foregoing detailed description has set forth various examples of the devices and/or processes via the use of block diagrams, flowcharts, and/or examples, such block diagrams, flowcharts, and/or examples contain one or more functions and/or operations, it will be understood by those within the art that each function and/or operation within such block diagrams, flowcharts, or examples can be implemented, individually and/or collectively, by a wide range of hardware, software, firmware, or virtually any combination thereof. In one example, several portions of the subject matter described herein may be implemented via Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), digital signal processors (DSPs), or other integrated formats. However, those skilled in the art will recognize that some aspects of the examples disclosed herein, in whole or in part, can be equivalently implemented in integrated circuits, as one or more computer programs running on one or more computers (e.g., as one or more programs running on one or more computer systems), as one or more programs running on one or more processors (e.g., as one or more programs running on one or more microprocessors), as firmware, or as virtually any combination thereof, and that designing the circuitry and/or writing the code for the software and or firmware would be well within the skill of one of skill in the art in light of this disclosure. For example, if a user determines that speed and accuracy are paramount, the user may opt for a mainly hardware and/or firmware vehicle; if flexibility is paramount, the user may opt for a mainly software implementation; or, yet again alternatively, the user may opt for some combination of hardware, software, and/or firmware.

In addition, those skilled in the art will appreciate that the mechanisms of the subject matter described herein are capable of being distributed as a program product in a variety of forms, and that an illustrative example of the subject matter described herein applies regardless of the particular type of signal bearing medium used to actually carry out the distribution. Examples of a signal bearing medium include, but are not limited to, the following: a recordable type medium such as a floppy disk, a hard disk drive, a Compact Disc (CD), a Digital Video Disk (DVD), a digital tape, a computer memory, etc.; and a transmission type medium such as a digital and/or an analog communication medium (e.g., a fiber optic cable, a waveguide, a wired communications link, a wireless communication link, etc.).

Those skilled in the art will recognize that it is common within the art to describe devices and/or processes in the fashion set forth herein, and thereafter use engineering practices to integrate such described devices and/or processes into data processing systems. That is, at least a portion of the devices and/or processes described herein can be integrated into a data processing system via a reasonable amount of experimentation. Those having skill in the art will recognize that a typical data processing system generally includes one or more of a system unit housing, a video display device, a memory such as volatile and non-volatile memory, processors such as microprocessors and digital signal processors, computational entities such as operating systems, drivers, graphical user interfaces, and applications programs, one or more interaction devices, such as a touch pad or screen, and/or control systems including feedback loops and control motors (e.g., feedback for sensing position and/or velocity; control motors for moving and/or adjusting components and/or quantities). A typical data processing system may be implemented utilizing any suitable commercially available components, such as those typically found in data computing/communication and/or network computing/communication systems.

The herein described subject matter sometimes illustrates different components contained within, or connected with, different other components.

Claim 1:
A computed implemented method (<NUM>) comprising:
providing at least one previously acquired quantitative image (302a) of a subject in a computer readable medium, each of the at least one quantitative images comprising elements, where a value of each element of the image (302a) has an absolute value which relates to a property of the subject (<NUM>) at a location corresponding to the element, the at least one quantitative image including at least one of quantitative MRI, quantitative PET, quantitative ultrasound or a whole body diffusion weighted imaging MRI;
providing an atlas (<NUM>) corresponding to the subject, the atlas comprising atlas elements (<NUM>), each of the atlas elements comprising a metric distribution (<NUM>);
determining a correspondence map relating elements of the at least one quantitative image (302a) to corresponding atlas elements (<NUM>) wherein determining the correspondence map comprises segmenting the at least one quantitative image (302a) into one or more structure elements and matching the one or more structure elements to the atlas elements (<NUM>) defined as structure elements;
determining candidate elements (<NUM>) by comparing a value of certain elements of the at least one quantitative image (302a) with the metric distribution (<NUM>) of related atlas elements (<NUM>) of the correspondence map;
providing at least one previously acquired additional image (302b) of the subject, each of the at least one additional images comprising elements, the at least one additional image including at least one non-quantitative image;
localizing the candidate elements (<NUM>) to corresponding elements of the at least one additional image (302b);
classifying the candidate elements (<NUM>) into at least one class based on properties of the corresponding elements of the at least one additional image (302b); and
determining a disease state of the candidate elements (<NUM>) by analyzing the at least one class of the at least one additional image (302b) corresponding to the candidate elements (<NUM>).