Patent Description:
The subject matter described herein relates generally to medical imaging and more specifically to computationally simulating images of anatomical structures and electrical activity to permit the accurate determination of subject <NUM>-dimensional anatomy and electrical rhythm diagnosis and source localization.

Medical imaging refers to techniques and processes for obtaining data characterizing a subject's internal anatomy and pathophysiology including, for example, images created by the detection of radiation either passing through the body (e.g. x-rays) or emitted by administered radiopharmaceuticals (e.g. gamma rays from technetium (99mTc) medronic acid given intravenously). By revealing internal anatomical structures obscured by other tissues such as skin, subcutaneous fat, and bones, medical imagining is integral to numerous medical diagnosis and/or treatments. Examples of medical imaging modalities include <NUM>-dimensional imaging such as: x-ray plain films; bone scintigraphy; and thermography, and <NUM>-dimensional imaging modalities such as: magnetic resonance imaging (MRI); computed tomography (CT), cardiac sestamibi scanning, and positron emission tomography (PET) scanning.

<CIT> describes an acute ischemia detection device comprising a <NUM>-lead electrocardiograph (ECG) device, an electronic data processing device, and a display component. The electronic data processing device applies <NUM>-lead ECG to vessel-specific lead (VSL) transforms to <NUM>-lead ECG data acquired by the <NUM>-lead ECG device to generate VSL lead data (e.g., LAD, LCX, and RCA vessel-specific lead data), determines ST values for the VSL lead data, and decides whether the <NUM>-lead ECG data acquired by the <NUM>-lead ECG device indicates acute ischemia by comparing the ST values for the VSL lead data with VSL lead ST thresholds. The display component may display an acute ischemia alarm or warning if the decision is the <NUM>-lead ECG data acquired by the <NUM>-lead ECG device indicates acute ischemia.

Systems, methods, and articles of manufacture, including computer program products, are provided for computationally simulating a three-dimensional representation of an anatomical structure. In particular, claim <NUM> defines a system and claim <NUM> defines a computer-implemented method.

Although widely available and less expensive, projectional, or <NUM>-dimensional, radiography techniques (e.g., X-ray plain films, gamma ray imaging (e.g. bone scintigraphy), fluoroscopy, and/or the like) are only able to generate two-dimensional images of a subject's internal anatomy, which may be inadequate for a variety of medical diagnosis and treatments. Conventional techniques for generating a three-dimensional representation of a subject's internal anatomy include computed tomography (CT) and magnetic resonance imaging (MRI). However, computed tomography and magnetic resonance imaging requires specialized equipment, trained technicians, often involves more time to obtain, and may be difficult to perform during invasive procedures or on critically ill subjects. As such, computed tomography and magnetic resonance imaging tend to be less accessible, more cost prohibitive, and often infeasible compared with projectional radiographs.

In some example embodiments, instead of relying on computed tomography or magnetic resonance imaging to obtain a three-dimensional representation of subject's internal anatomy, a simulated three-dimensional representation of a subject's internal anatomy may be determined based on one or more two-dimensional images of the subject's internal anatomy. For example, a simulated three-dimensional representation corresponding to the subject's internal anatomy may be identified based on one or more two-dimensional images of the subject's internal anatomy (e.g. <FIG>). The two-dimensional images of the subject's internal anatomy may be obtained using a projectional radiography technique including, for example, X-rays, gamma ray imaging (e.g. bone scintigraphy), fluoroscopy, and/or the like. Meanwhile, the simulated three-dimensional representation may be part of a library of simulated three-dimensional representations, each of which being associated with one or more corresponding two-dimensional images. For instance, one or more simulated radiograph images (e.g., X-ray images, gamma ray images, and/or the like) may be generated based on each of the simulated three-dimensional representations included in the library. Accordingly, identifying the simulated three-dimensional representation corresponding to the subject's intemal anatomy may include matching the two-dimensional images of the subject's internal anatomy to the computed two-dimensional images associated with the simulated three-dimensional representation.

The library of simulated three-dimensional representations includes one or more existing three-dimensional representations of the internal anatomies of one or more reference subjects including, for example, computed tomography scans, magnetic resonance imaging scans, and/or the like. The reference subjects may exhibit a variety of different anatomical attributes including, for example, variations in skeletal properties (e.g., size, abnormalities, and/or the like), organ geometry (e.g., size, relative position, and/or the like), musculature, subcutaneous fat distribution, and/or the like. As such, the simulated three-dimensional representations included in the library may also depict a variety of different anatomical attributes. Furthermore, additional anatomical variations may be introduced into the library of simulated three-dimensional representations by at least generating, based on the existing three-dimensional representations, one or more simulated three-dimensional representations that include at least variation to the internal anatomy of the corresponding reference subject. For example, in one representation, a muscle (e.g. the pectoralis major muscle) may be <NUM> in thickness. In another representation, the muscle (e.g. the pectoralis major muscle) may be <NUM> in thickness. For instance, based on an existing three-dimensional representation of the internal anatomy of a reference subject, one or more additional simulated three-dimensional representations may be generated to include variations in the skeletal properties (e.g., size, abnormalities, and/or the like), organ geometries (e.g., size, relative position, and/or the like), musculature, and/or subcutaneous fat distribution of the same reference subject.

Each simulated three-dimensional representation included in the library may be associated with one or more computed two-dimensional images including, for example, X-ray images, gamma ray images, and/or the like. A computed two-dimensional image may be generated based at least on either (a) a density and/or radiation transmissivity of the different tissues forming each of the anatomical structures (e.g., organs) included in a corresponding simulated three-dimensional representation, or (b) the absorption rate of radiopharmaceuticals (e.g. technetium (99mTc) medronic acid and/or the like) by different tissues and the emission rate of the radiopharmaceutical. Moreover, multiple computed two-dimensional image may be generated for each simulated three-dimensional representation in order to capture different views of the simulated three-dimensional representation including, for example, a left anterior oblique view, a right anterior oblique view, a straight anterior-posterior view, and/or the like. For example, a simulated X-ray image of the simulated three-dimensional representation of a human torso may be generated based at least in part on the respective of density and/or radiation transmissivity of the various anatomical structures included in the human torso such as skin, bones, subcutaneous fat, visceral fat, heart, lungs, liver, stomach, intestines, and/or the like. In some variations, this may be accomplished using the software platform Blender (Blender Foundation, Amsterdam, Netherlands). In some variations, a <NUM>-dimensional model of the body may be loaded into Blender. Different tissues within the model may be assigned different light transmissivities (e.g. greater transmissivity for subcutaneous fat, less transmissivity for bone). A simulated light source may be placed on one side of the model, and a flat surface placed on the other side of the model. The transmission of light through the model is computed, and an image of the projection on the two dimensional surface is recorded. This image may be further manipulated (e.g. white-black inversion) to produce a simulated <NUM>-dimensional radiograph. As noted, in some example embodiments, the simulated three-dimensional representation corresponding to the subject's internal anatomy may be identified by least matching the two-dimensional images of the subject's internal anatomy to computed two-dimensional images associated with the simulated three-dimensional representation.

In some example embodiments, each of the simulated three-dimensional representation and the corresponding computed two-dimensional images included in the library may be associated with a diagnosis. As such, when the two-dimensional images (e.g., X-ray images, gamma ray images, and/or the like) of the subject is matched to computed two-dimensional images associated with a three-dimensional representation included in the library, a diagnosis for the subject may be determined based on the diagnosis that is associated with the computed two-dimensional images. For example, the subject may be determined to have dilated cardiomyopathy if the two-dimensional images of the subject is matched to the computed two-dimensional images associated with dilated cardiomyopathy. It should be appreciated that a two-dimensional image of the subject may be matched to one or more computed two-dimensional images by applying a variety of image comparison techniques including, for example, scale invariant feature transform (SIFT), speed up robust feature (SURF), binary robust independent elementary features (BRIEF), oriented FAST and rotated BRIEF (ORB), and/or the like. A match between a two-dimensional image of the subject and one or more computed two-dimensional images may further be determined by applying one or more machine learning-based image comparison techniques including, for example, autoencoders, neural networks, and/or the like.

For example, the match between the two-dimensional image and the one or more computed two-dimensional images may be determined by applying one or more convolutional neural networks, recurrent neural networks, and/or the like. The neural network may be trained based on training data that includes pairs of matching and/or non-matching two-dimensional images. Moreover, the neural network may be trained to examine features present in corresponding portions of the two-dimensional image of the subject and at least some of the computed two-dimensional images included in the library to determine a similarity metric between each pair of two-dimensional images.

In some example embodiments, the match between a two-dimensional image of the subject's internal anatomy and one or more computed two-dimensional images may be probabilistic. For example, when a two-dimensional image of the subject is matched to computed two-dimensional images, each of the computed two-dimensional images may be associated with a value (e.g., a similarity index and/or the like) indicating a closeness of the match between the two-dimensional image and the computed two-dimensional image. Moreover, multiple diagnosis, including a likelihood for each of the diagnosis, may be determined for the subject based on the diagnosis associated with each of the computed two-dimensional images. For instance, the diagnosis for the subject may include a first probability (e.g., an x-percentage likelihood) of the subject having dilated cardiomyopathy and a second probability (e.g., an x-percentage likelihood) of the subject having a pulmonary embolism based at least on the probabilistic match between the two-dimensional images of the subject and the computed two-dimensional images included in the library.

The electrical activities of an organ are typically measured by recording device having one more leads (e.g., pairs of electrodes measuring voltage changes), which may be placed on a surface of the body near the organ as in the case of electrocardiography (ECG) for measuring the electrical activities of the heart and electroencephalography (EEG) for measuring the electrical activities of the brain. Although a common diagnostic modality in medicine, surface recordings are associated with a number of limitations. For example, surface recordings (e.g., electrocardiography, electroencephalography, and/or the like) are performed under the assumption of a standard surface electrogram setup (e.g., lead placement) even though variations in actual lead position can alter the morphology of the resulting electrogram and/or vectorgram (e.g., electrocardiogram, electroencephalogram, vectorcardiogram, and/or the like). The morphology of the resulting electrogram can also be altered due to significant variations in individual anatomy (e.g. obesity and/or the like) and/or the presence of co-morbidities (e.g. the lung disease emphysema and/or the like), which vary the conduction of electrical signals through the body. These electrical alterations can introduce error into the diagnoses made based on the electrogram as well as the processes utilizing the electrical signals to map the organ's electrical activity (e.g. mapping the source of a cardiac arrhythmia and/or the like). As such, in some example embodiments, a subject-specific computational simulation environment that captures individual variations in body surface lead placement and subject anatomy may enable a more accurate calculation of the electrical activity of the organ (e.g. heart, brain, and/or the like). For instance, a customized computational simulation environment for a subject may be generated to include a three-dimensional representation of the internal anatomy (e.g. thoracic anatomy including the heart for measuring cardiac electrical activity) as described above. The electrical activities of an organ may be simulated based on the three-dimensional representation of the subject's internal anatomy. The simulated electrical activities may include normal electrical activations (e.g. sinus rhythm for the heart) as well as abnormal electrical activations (e.g. ventricular tachycardia). Moreover, one or more electrical properties of the organ may be determined based on the simulation of the electrical activities of the organ.

In some example embodiments, the placement of each lead of a recording device may be determined based on one or more two-dimensional images of the subject's internal anatomy. Based on the simulated electrical activities of the organ and the known locations for the leads on the surface of the subject's body, an output for the simulated recording device (e.g., the electrical signals that are detected at each electrogram lead) may be determined based on the corresponding simulated three-dimensional representation of the subject's internal anatomy to generate a simulated electrogram (e.g. a simulated electrocardiogram, a simulated electroencephalogram, and/or the like). Once the relationship between the simulated organ (e.g. heart) and simulated electrogram properties (e.g. nonstandard electrocardiogram lead positions) is determined, the relationship between each lead and the likely electrical activation pattern of the organ can be more accurately calculated. For example, the relationship between the simulated organ and the simulated electrogram properties may enable the generation of a subject-specific transformation matrix, or correction matrix, that accounts for variations in lead placement and subject anatomy. In some embodiments, the accuracy of the simulation algorithm applied to generate the simulated output may be improved by at least updating the simulation algorithm based on clinical data including actual measurements of the electrical activities of the subject's organ as measured from the body surface electrodes.

<FIG> depicts a system diagram illustrating an imaging system <NUM>, in accordance with some example embodiments. Referring to <FIG>, the imaging system <NUM> may include a simulation controller <NUM>, a client <NUM>, and a data store <NUM> storing an image library <NUM>. As shown in <FIG>, the simulation controller <NUM>, the client <NUM>, and the data store <NUM> may be communicatively coupled via a network <NUM>. The network <NUM> may be a wired and/or wireless network including, for example, a wide area network (WAN), a local area network (LAN), a virtual local area network (VLAN), a public land mobile network (PLMN), the Internet, and/or the like. Meanwhile, the data store <NUM> may be a database including, for example, a graph database, an in-memory database, a relational database, a non-SQL (NoSQL) database, and/or the like.

In some example embodiments, the simulation controller <NUM> may be configured to identify, based at least on one or more two-dimensional images of the subject's internal anatomy, a simulated three-dimensional representation in the image library <NUM> that corresponds to the subject's internal anatomy. For example, the simulation controller <NUM> may receive, from the client <NUM>, on or more two-dimensional images of the subject's internal anatomy, which may be generated using a projectional radiography technique including, for example, X-rays, gamma rays, fluoroscopy, thermography, and/or the like. The simulation controller <NUM> may identify the simulated three-dimensional representation as corresponding to the subject's internal anatomy based at least on the two-dimensional images of the subject's internal anatomy being matched with the computed two-dimensional images associated with the simulated three-dimensional representation.

To further illustrate, <FIG> depicts a block diagram illustrating an example of identifying a simulated three-dimensional representation corresponding to a subject's internal anatomy, in accordance with some example embodiments. Referring to <FIG>, the simulation controller <NUM> may receive, from the client <NUM>, one or more two-dimensional images depicting an internal anatomy of a subject <NUM> including, for example, a two-dimensional image <NUM>. The two-dimensional image <NUM> may be generated using a projectional radiography technique including, for example, X-rays, gamma rays, fluoroscopy, and/or the like. In some example embodiments, the simulation controller <NUM> may identify, based at least on the two-dimensional image <NUM>, one or more simulated three-dimensional representations in the image library <NUM> that corresponds to the internal anatomy of the subject <NUM>.

Referring again to <FIG>, the image library <NUM> may include a plurality of simulated three-dimensional representations including, for example, a first simulated three-dimensional representation 220a, a second simulated three-dimensional representation 220b, a third simulated three-dimensional representation 220c, and/or the like. As shown in <FIG>, each simulated three-dimensional representation included in the image library <NUM> may be associated with one or more computed two-dimensional images, each of which being generated based on a corresponding simulated three-dimensional representation. For example, <FIG> shows the first simulated three-dimensional representation 220a being associated with a first computed two-dimensional image 225a generated based on the first simulated three-dimensional representation 220a, the second simulated three-dimensional representation 220b being associated with a second computed two-dimensional image 225b generated based on the second simulated three-dimensional representation 220b, and the third simulated three-dimensional representation 220c being associated with a third computed two-dimensional image 225c generated based on the third simulated three-dimensional representation 220c.

The simulation controller <NUM> may apply one or more image comparison techniques in order to determine whether the two-dimensional image <NUM> matches the first computed two-dimensional image 225a associated with the first simulated three-dimensional representation 220a, the second computed two-dimensional image 225b associated with the second simulated three-dimensional representation 220b, and/or the third computed two-dimensional image 225c associated with the third simulated three-dimensional representation 220c. The one or more image comparison techniques may include scale invariant feature transform (SIFT), speed up robust feature (SURF), binary robust independent elementary features (BRIEF), oriented FAST and rotated BRIEF (ORB), and/or the like. Alternatively and/or additionally, the one or more image comparison techniques may include one or more machine learning models trained to identify similar images including, for example, autoencoders, neural networks, and/or the like.

In some example embodiments, the simulation controller <NUM> may apply the one or more image comparison techniques to generate a probabilistic match between the two-dimensional image <NUM> and one or more of the first computed two-dimensional image 225a, the second computed two-dimensional image 225b, and the third computed two-dimensional image 225c. As shown in <FIG>, each of the first computed two-dimensional image 225a, the second computed two-dimensional image 225b, and the third computed two-dimensional image 225c may be a similarity index and/or another value indicating a closeness of the match to the two-dimensional image <NUM>. For example, the simulation controller <NUM> may determine that the first computed two-dimensional image 225a is <NUM>% similar to the two-dimensional image <NUM>, the second computed two-dimensional image 225b is <NUM>% similar to the two-dimensional image <NUM>, and the third computed two-dimensional image 225c is <NUM>% similar to the two-dimensional image <NUM>. The simulation controller <NUM> may determine, based at least on the respective similarity index, that one or more of the first computed two-dimensional image 225a, the second computed two-dimensional image 225b, and the third computed two-dimensional image 225c match the two-dimensional image <NUM>. For instance, the simulation controller <NUM> may determine that the first computed two-dimensional image 225a matches the two-dimensional image <NUM> based on the first computed two-dimensional image 225a being associated with a highest similarity index and/or the first computed two-dimensional image 225a being associated with a similarity index exceeding a threshold value.

In some example embodiments, the simulation controller <NUM> may identify, based at least on the computed two-dimensional images matched to the two-dimensional image <NUM>, one or more simulated three-dimensional representations corresponding to the internal anatomy of the subject <NUM>. For example, based on the first computed two-dimensional image 225a being determined to match the two-dimensional image <NUM>, the simulation controller <NUM> may identify the first simulated three-dimensional representation 220a as corresponding to the internal anatomy of the subject <NUM>.

Furthermore, as shown in <FIG>, each of the first simulated three-dimensional representation 220a, the second simulated three-dimensional representation 220b, and the third simulated three-dimensional representation 220c may be associated with a diagnosis. As such, the simulation controller <NUM> may further determine one or more diagnosis for the subject <NUM> based at least on the one or more simulated three-dimensional representations determined to correspond to the internal anatomy of the subject <NUM>. When the simulation controller <NUM> determines multiple diagnosis for the subject <NUM>, each diagnosis may be associated with a probability corresponding to the similarity index between the two-dimensional image <NUM> and the computed two-dimensional image matched with the two-dimensional image <NUM>. For example, based on the <NUM>% similarity between the two-dimensional image <NUM> and the first computed two-dimensional image 225a, the simulation controller <NUM> may determine that there is a <NUM>% chance of the subject <NUM> being afflicted with dilated cardiomyopathy. Alternatively and/or additionally, based on the <NUM>% similarity between the two-dimensional image <NUM> and the second computed two-dimensional image 225b, the simulation controller <NUM> may determine that there is a <NUM>% chance of the subject <NUM> being afflicted with a pulmonary embolism.

In some example embodiments, an actual diagnosis for the subject <NUM> may be used to at least refine one or more machine learning-based image comparison techniques for matching the two-dimensional image <NUM> to one or more of the first computed two-dimensional image 225a, the second computed two-dimensional image 225b, and the third computed two-dimensional image 225c. For instance, if the simulation controller <NUM> applying a trained machine learning model (e.g., autoencoder, neural network, and/or the like) determines that the two-dimensional image <NUM> is matched to the first computed two-dimensional image 225a corresponding to dilated cardiomyopathy but the actual diagnosis for the subject <NUM> is a rib fracture, the simulation controller <NUM> may at least retrain the machine learning model to correctly match the two-dimensional image <NUM> to the third computed two-dimensional image 225c. The machine learning model may be retrained based on additional training data that include at least some two-dimensional images that depict a rib fracture. The retraining of the machine learning model may include further updating the one or more weights and/or biases applied by the machine learning model to reduce an error in an output of the machine learning model including, for example, the mismatching of two-dimensional images depicting rib fractures.

In order to reduce the time and computation resources associated with searching the image library <NUM> for one or more computed two-dimensional images matching the two-dimensional image <NUM>, the simulation controller <NUM> may apply one or more filters to eliminate at least some of the computed two-dimensional images from the search. For example, the computed two-dimensional images (and the corresponding simulated three-dimensional representations) included in the image library <NUM> may be indexed based on one or more attributes such as, for example, the demographics (e.g., age, gender, and/or the like) and/or the vital statistics (e.g., height, weight, and/or the like) of reference subjects depicted in the computed two-dimensional image. Alternatively and/or additionally, the computed two-dimensional images (and the corresponding simulated three-dimensional representations) included in the image library <NUM> may be indexed based on the corresponding primary symptom and/or complaint of the subject. For example, the first computed two-dimensional image 225a, the second computed two-dimensional image 225b, and the third computed two-dimensional image 225c may be indexed based on the complaint or symptom of "chest discomfort. " Alternatively and/or additionally, the computed two-dimensional images (and the corresponding simulated three-dimensional representations) included in the image library <NUM> may be indexed based on the corresponding diagnosis and/or types of diagnosis. For instance, the first computed two-dimensional image 225a and the second computed two-dimensional image 225b may be indexed as "heart conditions" while the third computed two-dimensional image 225c may be indexed as "bone fractures.

Accordingly, instead of comparing the two-dimensional image <NUM> to every computed two-dimensional image included in the image library <NUM>, the simulation controller <NUM> may eliminate, based on the demographics and/or the vital statistics of the subject <NUM>, one or more computed two-dimensional images of reference subjects having different demographics and/or vital statistics than the subject <NUM>. Alternatively and/or additionally, the simulation controller <NUM> may further eliminate, based on one or more symptoms of the subject <NUM>, one or more computed two-dimensional images associated with diagnosis that are inconsistent with the symptoms of the subject <NUM>.

Referring again to <FIG>, the image library <NUM> may include a plurality of simulated three-dimensional representations including, for example, the first simulated three-dimensional representation 220a, the second simulated three-dimensional representation 220b, the third simulated three-dimensional representation 220c, and/or the like. In some example embodiments, the first simulated three-dimensional representation 220a, the second simulated three-dimensional representation 220b, and/or the third simulated three-dimensional representation 220c may be existing three-dimensional representations of the internal anatomies of one or more reference subjects including, for example, computed tomography scans, magnetic resonance imaging scans, and/or the like. The reference subjects may exhibit a variety of different anatomical attributes including, for example, variations in skeletal properties (e.g., size, abnormalities, and/or the like), organ geometry (e.g., size, relative position, and/or the like), musculature, subcutaneous fat distribution, and/or the like. As such, the first simulated three-dimensional representation 220a, the second simulated three-dimensional representation 220b, and/or the third simulated three-dimensional representation 220c may also depict a variety of different anatomical attributes.

According to some example embodiments, additional anatomical variations may be introduced computationally into the image library <NUM> by at least generating, based on the existing three-dimensional representations, one or more simulated three-dimensional representations that include at least variation to the internal anatomy of the corresponding reference subject. For instance, the first simulated three-dimensional representation 220a, the second simulated three-dimensional representation 220b, and/or the third simulated three-dimensional representation 220c may be generated, based on one or more existing three-dimensional representations of the internal anatomy of a reference subject, to include variations in the skeletal properties (e.g., size, abnormalities, and/or the like), organ geometries (e.g., size, relative position, and/or the like), musculature, and/or subcutaneous fat distribution of the same reference subject.

To further illustrate, <FIG> and <FIG> depicts examples of simulated three-dimensional representations of internal anatomies, in accordance with some example embodiments. <FIG> and <FIG> depict examples of simulated three-dimensional representations that may be generated based on existing three-dimensional representations of the internal anatomies of one or more reference subjects including, for example, computed tomography scans, magnetic resonance imaging scans, and/or the like. Furthermore, <FIG> and <FIG> depict examples of simulated three-dimensional representations with computationally introduced anatomical variations including, for example, variations in skeletal properties (e.g., size, abnormalities, and/or the like), organ geometries (e.g., size, relative position, and/or the like), musculature, subcutaneous fat distribution, and/or the like.

For example, <FIG> depict examples of simulated three-dimensional representations of skeletal anatomy, in accordance with some example embodiments. <FIG> may depict a simulated three-dimensional representation <NUM> of the skeletal anatomy of a first reference subject who is a <NUM> years old, male, <NUM> feet <NUM> inches tall, weighing <NUM> pounds, and having severe congestive heart failure with a left ventricular ejection fraction of <NUM>%. <FIG> may depict a simulated three-dimensional representation <NUM> of the skeletal anatomy of a second reference subject who is <NUM> years old, female, <NUM> feet <NUM> inches tall, weighing <NUM> pounds, and having moderate chronic systolic congestive heart failure with a left ventricular ejection fraction of <NUM>%. Furthermore, <FIG> may depict a simulated three-dimensional representation <NUM> of the skeletal anatomy of a third reference subject who is <NUM> years old, weighing <NUM> pounds, and having a congenital heart disease with an ejection fraction of <NUM>%. As noted, <FIG> may be indexed based on one or more attributes including, for example, the demographics (e.g., age, gender, and/or the like), the vital statistics (e.g., weight, height, and/or the like), and/or the condition of the corresponding reference subject.

<FIG> depicts examples of simulated three-dimensional representations of cardiac anatomies, in accordance with some example embodiments. <FIG> depicts a simulated three-dimensional representation <NUM> of a heart with moderate congestive heart failure, an ejection fraction of <NUM>%, and a ventricular axis of <NUM> degrees (shown as a black line) in the frontal plane. <FIG> depicts a simulated three-dimensional representation <NUM> of a heart with a normal ejection fraction of <NUM>% and a ventricular axis of <NUM> degrees (shown as a black line) in the frontal plane. Furthermore, <FIG> depicts a simulated three-dimensional representation <NUM> of a heart with severe left ventricular dysfunction, an ejection fraction of <NUM>%, and a ventricular axis of <NUM> degrees (shown as a black line) in the frontal plane. <FIG> may also be indexed based on one or more attributes including, for example, the demographics (e.g., age, gender, and/or the like), the vital statistics (e.g., weight, height, and/or the like), and/or the condition of the corresponding reference subject.

As noted, the simulated three-dimensional representations included in the image library <NUM> may be used to generate the computed two-dimensional images included in the image library <NUM>. For example, referring again to <FIG>, the first computed two-dimensional image 225a may be generated based on the first simulated three-dimensional representation 220a, the second computed two-dimensional image 225b may be generated based on the second simulated three-dimensional representation 220b, and the third computed two-dimensional image 225c may be generated based on the third simulated three-dimensional representation 220c.

The computed two-dimensional images included in the image library <NUM> may correspond to radiograph images (e.g., X-ray images, gamma ray images, fluoroscopy images, and/or the like), which are typically captured using a projectional, or <NUM>-dimensional radiography techniques, in which at least a portion of a subject is exposed to electromagnetic radiation (e.g., X-rays, gamma rays, and/or the like). As such, in some example embodiments, a computed two-dimensional image may be generated by at least simulating the effects of being exposed to a radiation source. For example, the computed two-dimensional image based at least on a density and/or radiation transmissivity of the different tissues included in the simulated three-dimensional representation.

To further illustrate, <FIG> depicts an example of a technique for generating a computed two-dimensional image, in accordance with some example embodiments. Referring to <FIG>, a computed two-dimensional image <NUM> may be generated (e.g. using the software Blender (Blender Foundation, Amsterdam, Netherlands)) by at least simulating the effects of exposing, to a simulated radiation source <NUM> (e.g. light), a simulated three-dimensional representation <NUM> of an internal anatomy (e.g., a thoracic cavity and/or the like). The computed two-dimensional image <NUM> may be generated by at least determining, based at least on a density and/or transmissivity of the different tissues included in the simulated three-dimensional representation <NUM>, a quantity of simulated radiation (e.g., from the simulated radiation source <NUM>) that is able to pass through the different tissues included in the simulated three-dimensional representation <NUM> onto a simulated surface. An image of this project is then recorded and further processed (e.g. white-black inversion) to form the computed two-dimensional image <NUM>.

In some example embodiments, a view of the simulated three-dimensional representation <NUM> (e.g., straight anterior-posterior, anterior oblique, and/or the like) that is captured in the computed two-dimensional image <NUM> may be varied by at least varying a position and/or an orientation of the simulated radiation source <NUM> relative of the simulated three-dimensional representation <NUM>. Accordingly, multiple computed two-dimensional image may be generated for each simulated three-dimensional representation in order to capture different views of the simulated three-dimensional representation including, for example, a left anterior oblique view, a right anterior oblique view, a straight anterior-posterior view, and/or the like.

As noted, the electrical activities of an organ (e.g., heart, brain, and/or the like) is typically measured by a recording device one or more body surface leads, which may be surface electrodes configured to measure voltage changes on the surface of the subject's skin corresponding to the electrical activities of the organ. For example, <FIG> depicts an example of a clinical two-dimensional image <NUM> showing a posterior-anterior (PA) view. Notably, <FIG> depicts the positions of a number of surface electrodes including, for example, a first surface electrode 615a, a second surface electrode 615b, and a third surface electrode 615c. It should be appreciated that one or more of the first surface electrode 615a, the second surface electrode 615b, and the third surface electrode 615c may be in a non-standard positions. <FIG> depicts another example of a clinical two-dimensional image <NUM> showing a left lateral view of the same subject. Again, the positions of several surface electrodes may also be observed in the clinical two-dimensional image <NUM>.

Additionally, <FIG> depicts an example of leads for measuring the electrical activities of the heart. As shown in <FIG>, a plurality of leads (e.g., V1, V2, V3, V4, V5, and V6) may be placed on the surface of the subject's skin. Each of the plurality of leads may be configured to measure a voltage change on the surface of the subject's skin that corresponds to the electrical activities of the subject's heart including, for example, the dipole that is created due to the successive depolarization and repolarization of the heart. The signal from each lead may be recorded, in combination with one or more other leads, to generate, for example, the electrocardiogram <NUM> shown in <FIG>, demonstrating normal sinus rhythm.

In some example embodiments, the simulation controller <NUM> may be further configured to simulate, based on a computed two-dimensional image and/or a simulated three-dimensional representation corresponding to a subject's internal anatomy, the electrical activities of an organ (e.g., heart, brain, gastrointestinal system, and/or the like). After determining the placement of each lead in a simulated recording device based on a computed two-dimensional image of the subject's internal anatomy as described previously, the output for the simulated recording device (e.g., the electrical signals that are detected at each lead) may be determined based on the corresponding simulated three-dimensional representation of the subject's internal anatomy to generate, for example, a simulated electrocardiogram, a simulated electroencephalogram, and/or the like. For instance, the spread of an electric potential across the subject's heart as well as the corresponding signals that may be detected on the surface of the subject's skin may be simulated based at least on the subject's anatomical attributes (e.g., skeletal properties, organ geometry, musculature, subcutaneous fat distribution, and/or the like) indicated by the simulated three-dimensional representation corresponding to the subject's internal anatomy.

Determining the relationship between the target organ's simulated electrical activity and the simulated body surface electrode readings, a subject-specific transformation matrix that accounts for variations in lead placement and subject anatomy may be computed. This subject-specific transformation matrix, or correction matrix, may be used to more accurately determine the precise electrical activation pattern and orientation of the organ. For example, the subject-specific transformation matrix may be applied to generate a corrected electrogram and/or a corrected vectorgram (e.g. a corrected electrocardiogram, a corrected electroencephalogram, a corrected vectorcardiogram, and/or the like). The corrected electrogram may lead to improved diagnostic output and improved mapping of the source of the cardiac arrhythmia.

<FIG> depicts a flowchart illustrating an example of an imaging process <NUM>, in accordance with some example embodiments. Referring to <FIG> and <FIG>, the process <NUM> may be performed by the simulation controller <NUM>. For example, the simulation controller <NUM> may perform the imaging process <NUM> in order to generate a three-dimensional representation of an internal anatomy of the subject <NUM> by at least identifying a simulated three-dimensional representation in the image library <NUM> that corresponds to the internal anatomy of the subject <NUM>. Alternatively and/or additionally, the imaging process <NUM> may be performed to determine, based on the simulated three-dimensional representation corresponding to the internal anatomy of the subject <NUM>, a diagnosis for the subject <NUM>. Furthermore, in some example embodiments, the simulation controller <NUM> may perform the imaging process <NUM> in order to simulate the electrical activities of one or more organs of the subject <NUM>.

At <NUM>, the simulation controller <NUM> may generate an image library including a plurality of simulated three-dimensional representations of internal anatomies that are each associated with a diagnosis and one or more computed two-dimensional images. For example, as shown in <FIG>, the image library <NUM> may include a plurality of simulated three-dimensional representations including, for example, the first simulated three-dimensional representation 220a, the second simulated three-dimensional representation 220b, the third simulated three-dimensional representation 220c, and/or the like. The first simulated three-dimensional representation 220a, the second simulated three-dimensional representation 220b, and/or the third simulated three-dimensional representation 220c may also depict a variety of different anatomical attributes. For instance, the first simulated three-dimensional representation 220a, the second simulated three-dimensional representation 220b, and/or the third simulated three-dimensional representation 220c may be existing three-dimensional representations of the internal anatomies of one or more reference subjects exhibiting a variety of different anatomical attributes including, for example, variations in skeletal properties (e.g., size, abnormalities, and/or the like), organ geometry (e.g., size, relative position, and/or the like), musculature, subcutaneous fat distribution, and/or the like. Alternatively and/or additionally, one or more anatomical variations may be introduced computationally into the first simulated three-dimensional representation 220a, the second simulated three-dimensional representation 220b, and/or the third simulated three-dimensional representation 220c.

In some example embodiments, the simulated three-dimensional representations included in the image library <NUM> may be used to generate the computed two-dimensional images included in the image library <NUM>. For example, referring again to <FIG>, the first computed two-dimensional image 225a may be generated based on the first simulated three-dimensional representation 220a, the second computed two-dimensional image 225b may be generated based on the second simulated three-dimensional representation 220b, and the third computed two-dimensional image 225c may be generated based on the third simulated three-dimensional representation 220c.

The first computed two-dimensional image 225a, the second computed two-dimensional image 225b, and the third computed two-dimensional image 225c may each be generated by exposing, to a simulated radiation source, the corresponding first simulated three-dimensional representation 220a, the second simulated three-dimensional representation 220b, and the third simulated three-dimensional representation 220c. For instance, the first computed two-dimensional image 225a may be generated by at least determining, based at least on a density and/or transmissivity of the different tissues included in the first simulated three-dimensional representation 220a, a quantity of radiation (e.g., from a simulated radiation source) that is able to pass through the different tissues included in the first simulated three-dimensional representation 220a to form the first computed two-dimensional image 225a. Alternatively and/or additionally, the second computed two-dimensional image 225b may be generated by at least determining, based at least on a density and/or transmissivity of the different tissues forming each of the anatomical structures (e.g., organs) included in the second simulated three-dimensional representation 220b, a quantity of radiation (e.g., from a simulated radiation source) that is able to pass through the different tissues included in the second simulated three-dimensional representation 220b to form the second computed two-dimensional image 225b.

Furthermore, in some example embodiments, each of the simulated three-dimensional representations and the corresponding computed two-dimensional images included in the image library <NUM> may be associated with a primary symptom or complaint as well as a diagnosis. For example, the first computed two-dimensional image 225a, the second computed two-dimensional image 225b, and the third computed two-dimensional image 225c may be associated with the complaint or symptom of "chest discomfort. " Moreover, the first simulated three-dimensional representation 220a (and the first computed two-dimensional image 225a) may be associated with a diagnosis of dilated cardiomyopathy, the second simulated three-dimensional representation 220b (and the second computed two-dimensional image 225b) may be associated with a diagnosis of a pulmonary embolism, and the third simulated three-dimensional representation 220c (and the third computed two-dimensional image 225c) may be associated with a diagnosis of a rib fracture.

At <NUM>, the simulation controller <NUM> may identify, in the image library, a simulated three-dimensional representation corresponding to an internal anatomy of a subject based at least on a match between a computed two-dimensional image corresponding to the simulated three-dimensional representation and a two-dimensional image of the internal anatomy of the subject. For example, the simulation controller <NUM> may apply one or more image comparison techniques in order to determine whether the two-dimensional image <NUM> matches the first computed two-dimensional image 225a associated with the first simulated three-dimensional representation 220a, the second computed two-dimensional image 225b associated with the second simulated three-dimensional representation 220b, and/or the third computed two-dimensional image 225c associated with the third simulated three-dimensional representation 220c. The one or more image comparison techniques may include scale invariant feature transform (SIFT), speed up robust feature (SURF), binary robust independent elementary features (BRIEF), oriented FAST and rotated BRIEF (ORB), and/or the like. Alternatively and/or additionally, the one or more image comparison techniques may include one or more machine learning models trained to identify similar images including, for example, autoencoders, neural networks, and/or the like.

In some example embodiments, the match between the two-dimensional image <NUM> and one or more of the first computed two-dimensional image 225a, the second computed two-dimensional image 225b, and the third computed two-dimensional image 225c may be probabilistic. For example, as shown in <FIG>, the simulation controller <NUM> may determine that the first computed two-dimensional image 225a is <NUM>% similar to the two-dimensional image <NUM>, the second computed two-dimensional image 225b is <NUM>% similar to the two-dimensional image <NUM>, and the third computed two-dimensional image 225c is <NUM>% similar to the two-dimensional image <NUM>. The simulation controller <NUM> may determine, based at least on a computed two-dimensional image having a highest similarity index and/or a similarity index exceeding a threshold value, that one or more of the first computed two-dimensional image 225a, the second computed two-dimensional image 225b, and the third computed two-dimensional image 225c match the two-dimensional image <NUM>.

In some example embodiments, the time and computation resources associated with searching the image library <NUM> for one or more computed two-dimensional images matching the two-dimensional image <NUM> may be reduced by applying one or more filters to eliminate at least some of the computed two-dimensional images from the search. For example, the computed two-dimensional images (and the corresponding simulated three-dimensional representations) included in the image library <NUM> may be indexed based on one or more attributes such as, for example, the demographics (e.g., age, gender, and/or the like) and/or the vital statistics (e.g., height, weight, and/or the like) of reference subjects depicted in the computed two-dimensional image. Alternatively and/or additionally, the computed two-dimensional images (and the corresponding simulated three-dimensional representations) included in the image library <NUM> may be indexed based on the corresponding diagnosis and/or types of diagnosis.

Accordingly, instead of comparing the two-dimensional image <NUM> to every computed two-dimensional image included in the image library <NUM>, the simulation controller <NUM> may eliminate, based on the demographics, the vital statistics, and/or the symptoms of the subject <NUM>, one or more computed two-dimensional images of reference subjects having different demographics, different vital statistics, and/or diagnosis that are inconsistent with the symptoms of the subject <NUM>. For example, if the subject <NUM> exhibits symptoms consistent with a heart condition, the image library <NUM> may exclude, from the search of the image library <NUM>, the third computed two-dimensional image 225c based at least on the third computed two-dimensional image 225c being associated with a diagnosis (e.g., rib fracture) that is inconsistent with the symptoms of the subject <NUM>.

At <NUM>, the simulation controller <NUM> may generate a first output including the simulated three-dimensional representation corresponding to the internal anatomy of the subject and/or a diagnosis associated with the simulated three-dimensional representation. For example, in response to the two-dimensional image <NUM> of the subject <NUM> being matched to the first computed two-dimensional image 225a, the simulation controller <NUM> may generate an output including the first simulated three-dimensional representation 220a and/or the diagnosis (e.g., dilated cardiomyopathy) associated with the first simulated three-dimensional representation 220a. The simulation controller <NUM> may generate the output to also include a value indicative of the closeness of the match (e.g., <NUM>% similar) between the two-dimensional image <NUM> and the first computed two-dimensional image 225a. Alternatively and/or additionally, the simulation controller <NUM> may generate the output to include a value indicative of a probability of the diagnosis associated with the first simulated three-dimensional representation 220a (e.g., <NUM>% chance of dilated cardiomyopathy).

It should be appreciated that the simulation controller <NUM> may send, to the client <NUM>, the first output including the simulated three-dimensional representation corresponding to the internal anatomy of the subject and/or a diagnosis associated with the simulated three-dimensional representation. Alternatively and/or additionally, the simulation controller <NUM> may generate a user interface configured to display, at the client <NUM>, the first output including the simulated three-dimensional representation corresponding to the internal anatomy of the subject and/or a diagnosis associated with the simulated three-dimensional representation.

At <NUM>, the simulation controller <NUM> may determine, based at least on one or more clinical two-dimensional images of the subject and the simulated three-dimensional representation corresponding to the internal anatomy of the subject, a lead placement for a recording device measuring an electrical activity of an organ of the subject. For example, the lead placement for electrocardiography (ECG) to measure the electrical activities of the heart and/or electroencephalography (EEG) to measure the electrical activities of the brain may be determined based on the images <NUM> and <NUM> corresponding to <FIG> and <FIG>.

At <NUM>, the simulation controller <NUM> may generate, based at least on the lead placement and the simulated three-dimensional representation corresponding to the internal anatomy of the subject, a second output including the lead placement and a simulation of the electrical activities measured by the recording device. For example, in some example embodiments, the simulation controller <NUM> may further determine, based at least on the lead placement (e.g., determined at operation <NUM>) and the first simulated three-dimensional representation 220a corresponding to the internal anatomy of the subject <NUM>, a simulated electrocardiogram (ECG) depicting the electrical activities of the heart and/or a simulated electroencephalography (EEG) depicting the electrical activities of the brain. The simulated electrocardiogram (ECG) and/or the simulated electroencephalography (EEG) may depict the signals that may be measured by each lead placed in accordance with the placement determined in operation <NUM>. For instance, a simulated electrocardiogram may depict the voltage changes that may be measured by each lead on the surface of the subject's skin. These voltage changes may correspond to the electrical activities of the subject's heart including, for example, the dipole that is created due to the successive depolarization and repolarization of the heart.

In some example embodiments, the simulation controller <NUM> may send, to the client <NUM>, the second output including the lead placement and/or the simulation of the electrical activities measured by the recording device. Alternatively and/or additionally, the simulation controller <NUM> may generate a user interface configured to display, at the client <NUM>, the second output including the lead placement and/or the simulation of the electrical activities measured by the recording device.

<FIG> depicts a flowchart illustrating another example of an imaging process <NUM>, in accordance with some example embodiments. Referring to <FIG> and <FIG>, the process <NUM> may be performed by the simulation controller <NUM>. For example, the simulation controller <NUM> may perform the imaging process <NUM> in order to generate a three-dimensional representation of an internal anatomy of the subject <NUM> by at least identifying a simulated three-dimensional representation in the image library <NUM> that corresponds to the internal anatomy of the subject <NUM>. Alternatively and/or additionally, the imaging process <NUM> may be performed to determine, based on the simulated three-dimensional representation corresponding to the internal anatomy of the subject <NUM>, a diagnosis for the subject <NUM>. Furthermore, in some example embodiments, the simulation controller <NUM> may perform the imaging process <NUM> in order to simulate the electrical activities of one or more organs of the subject <NUM> to produce a customized simulation environment of the subject including the electrical activity of an organ and the simulated body surface electrical activity including the simulated body surface recordings detected by the recording electrodes (bottom right box labelled Product <NUM>).

As shown in <FIG>, the simulation controller <NUM> may receive inputs including (<NUM>) demographic and clinical information such as age, weight, sex, clinical situation, and symptoms; (<NUM>) two-dimensional clinical images from one or more views (examples include <FIG>); and (<NUM>) subject electrical recordings (e.g. a clinical electrogram or vectorgram such as, for example, a clinical electrocardiogram, electroencephalogram, vectorcardiogram, and/or the like).

In some example embodiments, the image library <NUM> may be created from subject-derived, three-dimensional representations of subject anatomy. The simulated two-dimensional images may be created to include simulated two-dimensional images from different angles. Moreover, the simulated two-dimensional images and the corresponding three-dimensional models may be indexed with one or more subject attributes including, for example, weight, height, sex, clinical situation, symptoms, and/or the like.

For a specific subject, the simulation controller may receive inputs including, for example, the subject's age, weight, height, sex, clinical situation, and symptoms (<FIG>, Input <NUM>). The simulation controller <NUM> may select an appropriate simulation library (<FIG>, face symbol) for the intended instance (<FIG>, Intermediate Product <NUM>). Furthermore, the simulation controller <NUM> may receive one or more two-dimensional images of the subject's anatomy (<FIG>, Input <NUM>) and compares these two-dimensional images to the computed two-dimensional images included in the image library <NUM>. Computed two-dimensional images with the highest correlation with the subject's two-dimensional images may be identified. A combination of the highest matching computed two-dimensional images, the corresponding three-dimensional representations, and the associated case information (e.g., demographics, clinical situation, diagnosis, and/or the like) may be output by the simulation controller <NUM> (<FIG>, Product <NUM>).

In some example embodiments, the simulation controller <NUM> may further identify the locations of one or more leads (e.g., pairs of surface electrodes) in the subject's two-dimensional images and calculates positions of the leads relative to the subject's skin (<FIG>, Intermediate Product <NUM>). The simulation controller <NUM> may compute the angular and spatial relationship between the actual lead placement, the target organ (e.g., heart, brain, and/or the like), and the position of standard lead placements, thereby creating a subject-specific three-dimensional simulation environment suitable for simulating the electrical activities of the target organ (<FIG>, Intermediate Product <NUM>).

A simulation of the electrical activation of the organ may be performed within the subject-specific three-dimensional simulation environment including the three-dimensional representation corresponding to the subject's internal anatomy. For example, the simulated electrical field from the organ may be calculated as the electrical field diffuses through body tissues to the skin surface. Simulated recordings at both the subject-specific electrode positions and standard electrode positions may be computed. The relationship between the organ's electrical activation and the body surface recordings may be used to compute correction function for each electrode site (e.g. a "nonstandard-to-standard correction matrix") and for correcting between the organ's electrical activation pattern and that observed at the body surface (e.g. a "vectorgram correction matrix").

The subject's recorded electrogram is then analyzed. Using the correction matrices, a standardized electrogram (e.g. <FIG>, Product <NUM>) and/or a spatially and rotationally-corrected vectorgram (e.g. <FIG>, Product <NUM>) may be generated. The standardized electrogram may be used to increase the diagnostic accuracy of the recorded electrogram while the corrected vectorgram may be used to increase the accuracy of an arrhythmia source localization system.

It should be appreciated that the simulation controller <NUM> may operate (<NUM>) to create a simulated three-dimensional representation of a subject's internal anatomy as well as a computational assessment of diagnosis probability (<FIG>: Potential Use <NUM>); (<NUM>) to convert a nonstandard electrogram (e.g. nonstandard <NUM>-lead electrocardiogram) to a standard electrogram (e.g. standard <NUM>-lead electrocardiogram) (<FIG>: Potential Use <NUM>) to improve the diagnostic accuracy of the electrogram; and (<NUM>) to correct for subject-specific variations in electrode position and subject anatomy in the calculation of a three-dimensional vectorgram (e.g., vectorcardiogram and/or the like) to permit an accurate electrical source mapping (e.g. for use in arrhythmia source localization) (<FIG>, Potential Use <NUM>).

<FIG> depicts a block diagram illustrating an example of process <NUM> for generating a corrected electrogram, in accordance with some example embodiments. Referring to <FIG> and <FIG>, the process <NUM> may be performed by the simulation controller <NUM> in order to generate a corrected electrogram that accounts for variations in lead placement and subject anatomy.

As shown in <FIG>, the simulation controller <NUM> may generate, based at least on a simulated three-dimensional representation of the subject's internal anatomy (e.g., thorax cavity and/or the like), a rhythm simulation (e.g., ventricular tachycardia and/or the like). The simulated three-dimensional representation of the subject's internal anatomy may be identified based on one or more clinical two-dimensional images of the subject's internal anatomy. Moreover, a first plurality of surface electrode recordings may be computed based on the rhythm simulation to account for subject-specific lead placements, which may deviate from standard lead placements. A second plurality of surface electrode recordings corresponding to standard lead placements may also be computed based on the rhythm simulation.

In some example embodiments, a transformation matrix A may be generated based on a difference between the first plurality of surface electrode recordings and the second plurality of surface electrode recordings. The transformation matrix A may capture variations in lead placement as well as subject anatomy. Accordingly, the transformation matrix A may be applied to a clinical electrogram (e.g., a clinical electrocardiogram, a clinical electroencephalogram, and/or the like) to generate a corrected electrogram (e.g., a corrected electrogram, a corrected electroencephalogram, and/or the like) by at least removing, from the clinical electrogram, deviations that are introduced by non-standard lead placement and/or anatomical variations.

<FIG> a block diagram illustrating an example of process <NUM> for generating a corrected vectorgram, in accordance with some example embodiments. Referring to <FIG> and <FIG>, the process <NUM> may be performed by the simulation controller <NUM> in order to generate a corrected electrogram that accounts for variations in lead placement and subject anatomy.

As shown in <FIG>, the simulation controller <NUM> may generate, based at least on a simulated three-dimensional representation of the subject's internal anatomy (e.g., thorax cavity and/or the like), a rhythm simulation (e.g., ventricular tachycardia and/or the like). The simulated three-dimensional representation of the subject's internal anatomy may be identified based on one or more clinical two-dimensional images of the subject's internal anatomy. Further based on the rhythm simulation, the simulation controller <NUM> may generate a simulated three-dimensional electrical properties of a target organ (e.g., heart, brain, and/or the like) as well as a simulation of body surface electrical potentials and electrical recordings. A simulated three-dimensional vectorgram (e.g., a vectorcardiogram and/or the like) may be generated based on the simulated body surface recordings.

In some example embodiments, a transformation matrix A may be generated based on a difference between the simulated three-dimensional electrical properties of the target organ and the simulated body surface recordings. The transformation matrix A may capture variations in lead placement as well as subject anatomy. Accordingly, the transformation matrix A may be applied to a clinical vectorgram (e.g., a clinical vectorcardiogram and/or the like) to generate a corrected vectorgram (e.g., a corrected vectorcardiogram and/or the like) by at least removing, from the clinical vectorcardiogram, deviations arising from non-standard lead placement and/or anatomical variations.

<FIG> depicts a block diagram illustrating a computing system <NUM>, in accordance with some example embodiments. Referring to <FIG> and <FIG>, the computing system <NUM> can be used to implement the simulation controller <NUM> and/or any components therein.

As shown in <FIG>, the computing system <NUM> can include a processor <NUM>, a memory <NUM>, a storage device <NUM>, and input/output device <NUM>. The processor <NUM>, the memory <NUM>, the storage device <NUM>, and the input/output device <NUM> can be interconnected via a system bus <NUM>. The processor 1010is capable of processing instructions for execution within the computing system <NUM>. Such executed instructions can implement one or more components of, for example, the simulation controller <NUM>. In some implementations of the current subject matter, the processor <NUM> can be a single-threaded processor. Alternately, the processor <NUM> can be a multi-threaded processor. The processor <NUM> is capable of processing instructions stored in the memory <NUM> and/or on the storage device <NUM> to display graphical information for a user interface provided via the input/output device <NUM>.

The memory <NUM> is a computer readable medium such as volatile or nonvolatile that stores information within the computing system <NUM>. The memory <NUM> can store data structures representing configuration object databases, for example. The storage device <NUM> is capable of providing persistent storage for the computing system <NUM>. The storage device <NUM> can be a floppy disk device, a hard disk device, an optical disk device, or a tape device, or other suitable persistent storage means. The input/output device <NUM> provides input/output operations for the computing system <NUM>. In some implementations of the current subject matter, the input/output device <NUM> includes a keyboard and/or pointing device. In various implementations, the input/output device <NUM> includes a display unit for displaying graphical user interfaces.

According to some implementations of the current subject matter, the input/output device <NUM> can provide input/output operations for a network device. For example, the input/output device <NUM> can include Ethernet ports or other networking ports to communicate with one or more wired and/or wireless networks (e.g., a local area network (LAN), a wide area network (WAN), the Internet).

In some implementations of the current subject matter, the computing system <NUM> can be used to execute various interactive computer software applications that can be used for organization, analysis and/or storage of data in various (e.g., tabular) format. Alternatively, the computing system <NUM> can be used to execute any type of software applications. These applications can be used to perform various functionalities, e.g., planning functionalities (e.g., generating, managing, editing of spreadsheet documents, word processing documents, and/or any other objects, etc.), computing functionalities, communications functionalities, and/or the like. The applications can include various add-in functionalities or can be standalone computing products and/or functionalities. Upon activation within the applications, the functionalities can be used to generate the user interface provided via the input/output device <NUM>. The user interface can be generated and presented to a user by the computing system <NUM> (e.g., on a computer screen monitor, etc.).

These computer programs, which can also be referred to as programs, software, software applications, applications, components, or code, include machine instructions for a programmable processor, and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. The machine-readable medium can alternatively, or additionally, store such machine instructions in a transient manner, such as for example, as would a processor cache or other random access memory associated with one or more physical processor cores.

Claim 1:
A system (<NUM>; <NUM>), comprising:
at least one processor (<NUM>; <NUM>); and
at least one memory (<NUM>) including program code which when executed by the at least one processor (<NUM>; <NUM>) provides operations comprising:
receiving one or more two-dimensional images (<NUM>, <NUM>) of at least a portion of an anatomy of a subject (<NUM>) including a target organ, such as a patient's heart, and one or more electrogram leads (615a-c) provided on a body of the subject (<NUM>) according to a non-standard lead placement;
identifying a three-dimensional representation (220a-c) of the portion of the anatomy of the subject (<NUM>) including the target organ,
wherein the identifying includes comparing the two-dimensional image (<NUM>, <NUM>) to one or more two-dimensional images (225a-c) included in a library (<NUM>) mapping the one or more two-dimensional images (225a-c) to one or more corresponding three-dimensional representations (220a-c);
identifying the non-standard lead placement of the one or more electrogram leads (615a-c) on the body of the subject (<NUM>) based at least on an analysis of the two-dimensional image (<NUM>, <NUM>);
creating a subject-specific three-dimensional simulation environment including the three-dimensional representation (220a-c) for simulating electrical activations of the target organ;
generating, based on the subject-specific three-dimensional simulation environment, one or more simulated electrical activations of the target organ;
generating, based at least on the one or more simulated electrical activations, a non-standard electrogram associated with the non-standard lead placement of the one or more electrogram leads (615a-c) on the body of the subject (<NUM>);
generating, based at least on the one or more simulated electrical activations, a standard electrogram (<NUM>) associated with a standard lead placement of the one or more electrogram leads (615a-c) on the body of the subject (<NUM>); and
correcting, based at least on a difference between the nonstandard electrogram and the standard electrogram (<NUM>), an actual electrogram generated for the subject (<NUM>) using the non-standard lead placement.