Abstract: Methods and systems are described for assessing a vessel obstruction. The methods and systems obtain a volumetric image dataset of a myocardium and at least one coronary vessel, wherein the myocardium comprises muscular tissue of the heart. A three-dimensional (3D) image corresponding to a coronary vessel of interest is created from the volumetric image dataset. Feature data that represents features of both the myocardium and the coronary vessel of interest is generated. At least some of the feature data is determined by a first machine learning-based model based on the 3D image. A second machine learning-based model is used to determine at least one parameter based on the feature data, wherein the at least one parameter represents functionally significant coronary lesion severity of the coronary vessel of interest.

Abstract: Methods and systems are described for assessing a vessel obstruction. The methods and systems obtain a volumetric image dataset of a myocardium and at least one coronary vessel, wherein the myocardium comprises muscular tissue of the heart. A three-dimensional (3D) image corresponding to a coronary vessel of interest is created from the volumetric image dataset. Feature data that represents features of both the myocardium and the coronary vessel of interest is generated. At least some of the feature data is determined by a first machine learning-based model based on the 3D image. A second machine learning-based model is used to determine at least one parameter based on the feature data, wherein the at least one parameter represents functionally significant coronary lesion severity of the coronary vessel of interest.

Abstract: Methods and systems are described for assessing a vessel obstruction. The methods and systems obtain a volumetric image dataset of a myocardium and at least one coronary vessel, wherein the myocardium comprises muscular tissue of the heart. A three-dimensional (3D) image corresponding to a coronary vessel of interest is created from the volumetric image dataset. Feature data that represents features of both the myocardium and the coronary vessel of interest is generated. At least some of the feature data is determined by a first machine learning-based model based on the 3D image. A second machine learning-based model is used to determine at least one parameter based on the feature data, wherein the at least one parameter represents functionally significant coronary lesion severity of the coronary vessel of interest.

Abstract: Methods and systems are provided for assessing the presence of functionally significant stenosis in one or more coronary arteries, further known as a severity of vessel obstruction. The methods and systems can implement a prediction phase that comprises segmenting at least a portion of a contrast enhanced volume image data set into data segments corresponding to wall regions of the target organ, and analyzing the data segments to extract features that are indicative of an amount of perfusion experiences by wall regions of the target organ. The methods and systems can obtain a feature-perfusion classification (FPC) model derived from a training set of perfused organs, classify the data segments based on the features extracted and based on the FPC model, and provide, as an output, a prediction indicative of a severity of vessel obstruction based on the classification of the features.

Abstract: Methods and systems are provided for assessing the presence of functionally significant stenosis in one or more coronary arteries, further known as a severity of vessel obstruction. The methods and systems can implement a prediction phase that comprises segmenting at least a portion of a contrast enhanced volume image data set into data segments corresponding to wall regions of the target organ, and analysing the data segments to extract features that are indicative of an amount of perfusion experiences by wall regions of the target organ. The methods and systems can obtain a feature-perfusion classification (FPC) model derived from a training set of perfused organs, classify the data segments based on the features extracted and based on the FPC model, and provide, as an output, a prediction indicative of a severity of vessel obstruction based on the classification of the features.

Abstract: Methods and systems are described for assessing a vessel obstruction. The methods and systems obtain a volumetric image dataset of a myocardium and at least one coronary vessel, wherein the myocardium comprises muscular tissue of the heart. A three-dimensional (3D) image corresponding to a coronary vessel of interest is created from the volumetric image dataset. Feature data that represents features of both the myocardium and the coronary vessel of interest is generated. At least some of the feature data is determined by a first machine learning-based model based on the 3D image. A second machine learning-based model is used to determine at least one parameter based on the feature data, wherein the at least one parameter represents functionally significant coronary lesion severity of the coronary vessel of interest.

Abstract: Methods and systems are described for assessing a vessel obstruction. The methods and systems obtain a volumetric image dataset for a target organ that includes a vessel of interest, extract an axial trajectory extending along of a vessel of interest (VOI) within the volumetric image dataset, and create a three-dimensional (3D) multi-planer reformatted (MPR) image based on the volumetric image dataset and the axial trajectory of the VOI. The methods and systems also extract a VOI parameter from the MPR utilizing a machine learning-based vessel obstruction assessment (VOA) model. Methods and systems are also described for implementing a prediction phase to perform at least one of i) detecting plaque type, ii) classifying anatomical severity of vessel blockage, and/or iii) classifying a hemodynamic severity of vessel obstructions within an unseen portion of the volumetric image data set.

Abstract: Methods and systems are provided for assessing the presence of functionally significant stenosis in one or more coronary arteries, further known as a severity of vessel obstruction. The methods and systems can implement a prediction phase that comprises segmenting at least a portion of a contrast enhanced volume image data set into data segments corresponding to wall regions of the target organ, and analysing the data segments to extract features that are indicative of an amount of perfusion experiences by wall regions of the target organ. The methods and systems can obtain a feature-perfusion classification (FPC) model derived from a training set of perfused organs, classify the data segments based on the features extracted and based on the FPC model, and provide, as an output, a prediction indicative of a severity of vessel obstruction based on the classification of the features.

Abstract: Methods and systems are described for assessing a vessel obstruction. The methods and systems obtain a volumetric image dataset for a target organ that includes a vessel of interest, extract an axial trajectory extending along of a vessel of interest (VOI) within the volumetric image dataset, and create a three-dimensional (3D) multi-planer reformatted (MPR) image based on the volumetric image dataset and the axial trajectory of the VOI. The methods and systems also extract a VOI parameter from the MPR utilizing a machine learning-based vessel obstruction assessment (VOA) model. Methods and systems are also described for implementing a prediction phase to perform at least one of i) detecting plaque type, ii) classifying anatomical severity of vessel blockage, and/or iii) classifying a hemodynamic severity of vessel obstructions within an unseen portion of the volumetric image data set.

Abstract: Methods and systems are provided for assessing the presence of functionally significant stenosis in one or more coronary arteries, further known as a severity of vessel obstruction. The methods and systems can implement a prediction phase that comprises segmenting at least a portion of a contrast enhanced volume image data set into data segments corresponding to wall regions of the target organ, and analyzing the data segments to extract features that are indicative of an amount of perfusion experiences by wall regions of the target organ. The methods and systems can obtain a feature-perfusion classification (FPC) model derived from a training set of perfused organs, classify the data segments based on the features extracted and based on the FPC model, and provide, as an output, a prediction indicative of a severity of vessel obstruction based on the classification of the features.

Abstract: Methods and systems are provided for assessing the presence of functionally significant stenosis in one or more coronary arteries, further known as a severity of vessel obstruction. The methods and systems can implement a prediction phase that comprises segmenting at least a portion of a contrast enhanced volume image data set into data segments corresponding to wall regions of the target organ, and analysing the data segments to extract features that are indicative of an amount of perfusion experiences by wall regions of the target organ. The methods and systems can obtain a feature-perfusion classification (FPC) model derived from a training set of perfused organs, classify the data segments based on the features extracted and based on the FPC model, and provide, as an output, a prediction indicative of a severity of vessel obstruction based on the classification of the features.

Abstract: Methods and systems are provided for assessing the presence of functionally significant stenosis in one or more coronary arteries, further known as a severity of vessel obstruction. The methods and systems can implement a prediction phase that comprises segmenting at least a portion of a contrast enhanced volume image data set into data segments corresponding to wall regions of the target organ, and analyzing the data segments to extract features that are indicative of an amount of perfusion experiences by wall regions of the target organ. The methods and systems can obtain a feature-perfusion classification (FPC) model derived from a training set of perfused organs, classify the data segments based on the features extracted and based on the FPC model, and provide, as an output, a prediction indicative of a severity of vessel obstruction based on the classification of the features.

Abstract: Methods and systems are provided for assessing the presence of functionally significant stenosis in one or more coronary arteries, further known as a severity of vessel obstruction. The methods and systems can implement a prediction phase that comprises segmenting at least a portion of a contrast enhanced volume image data set into data segments corresponding to wall regions of the target organ, and analysing the data segments to extract features that are indicative of an amount of perfusion experiences by wall regions of the target organ. The methods and systems can obtain a feature-perfusion classification (FPC) model derived from a training set of perfused organs, classify the data segments based on the features extracted and based on the FPC model, and provide, as an output, a prediction indicative of a severity of vessel obstruction based on the classification of the features.