Abstract: Techniques for processing multiple cardiac images are disclosed. The multiple cardiac images, each of which depicts a portion of coronary arteries, i.e., the same portion of coronary arteries, within an anatomical region of interest, are obtained either during or after an angiography exam. A respective set of lumen radius measurements is determined based on each of the multiple cardiac images and comprises multiple lumen radius measurements respectively associated with multiple locations of the portion of the coronary arteries. A maximum stenosis severity profile and a minimum stenosis severity profile associated with the portion of the coronary arteries are respectively determined based on the respective sets of lumen radius measurements. A lumen radius profile associated with the portion of the coronary arteries is determined based on the maximum stenosis severity profile and the minimum stenosis severity profile.

Abstract: Techniques for adjusting or editing respective segments of a contour of a given lumen segmentation of a portion of coronary arteries are described. The respective segments of the contour are adjusted by processing multiple cardiac images. Each of the multiple cardiac images depicts a portion of coronary arteries, i.e., the same portion of coronary arteries, within an anatomical region of interest. Respective magnitudes of one or more local segmentation uncertainties are determined based on the multiple cardiac images. Each of the one or more local segmentation uncertainties is associated with a respective segment of the contour of the given lumen segmentation. The respective segments of the contour are adjusted edited or manipulated based on the respective magnitudes of the one or more local segmentation uncertainties.

Abstract: Techniques for processing multiple cardiac images are disclosed. The processing may take place either during or after an angiography exam of a coronary artery of interest. The multiple cardiac images are obtained either during or after the angiography exam. Each of the multiple cardiac images depicts a respective segment of the coronary artery of interest. A geometric structure of the coronary artery of interest is determined based on the multiple cardiac images. A lumped parameter model of the coronary artery of interest is determined based on the geometric structure, and respective values of at least one hemodynamic index at a position of the coronary artery of interest is determined based on the lumped parameter model of the coronary artery of interest.

Abstract: Various aspects of the disclosure generally pertain to determining estimates of hemodynamic properties based on angiographic x-ray examinations of a coronary system. Various aspects of the disclosure specifically pertain to determining such estimates based on single frame metrics operating on two-dimensional images. For example, the fractional flow reserve (FFR) can be computed.

Abstract: Anomalies in images are detected. A generative network and/or an autoencoder (“G/A-Network”), a Siamese network, a first training-dataset of normal images and a second training-dataset of abnormal images are provided. The G/A-network is trained to produce latent data from input images and output images from the latent data, wherein the training is performed with images of the first training-dataset, wherein a loss function is used for training at least at the beginning of training, and the loss function enhances the similarity of the input images and respective output images. The Siamese network is trained to generate similarity measures between input images and respective output images, wherein the training is performed with images of the first training-dataset and the second training-dataset in that images of both training-datasets are used as input images for the G/A-network and output images of the G/A-network are compared with their respective input images by the Siamese network.

Abstract: A first sequence of cardiac image frames are received by a first neural network of the neural network system. The first neural network outputs a first set of feature values. The first set of feature values includes a plurality of data subsets, each corresponding to a respective image frame and relating to spatial features of the respective image frame. The first set of feature values are received at a second neural network of the neural network system. The second neural network outputs a second set of feature values relating to temporal features of the spatial features. Based on the second set of feature values, a cardiac phase value relating to a cardiac phase associated with a first image frame is determined.

Abstract: A method, apparatus and non-transitory computer readable medium are for segmenting different types of structures, including cancerous lesions and regular structures like vessels and skin, in a digital breast tomosynthesis (DBT) volume. In an embodiment, the method includes: pre-classification of the DBT volume in dense and fatty tissue and based on the result; localizing a set of structures in the DBT volume by using a multi-stream deep convolutional neural network; and segmenting the localized structures by calculating a probability for belonging to a specific type of structure for each voxel in the DBT volume by using a deep convolutional neural network for providing a three-dimensional probabilistic map.

Abstract: A first sequence of cardiac image frames are received by a first neural network of the neural network system. The first neural network outputs a first set of feature values. The first set of feature values includes a plurality of data subsets, each corresponding to a respective image frame and relating to spatial features of the respective image frame. The first set of feature values are received at a second neural network of the neural network system. The second neural network outputs a second set of feature values relating to temporal features of the spatial features. Based on the second set of feature values, a cardiac phase value relating to a cardiac phase associated with a first image frame is determined.

Abstract: A method, apparatus and non-transitory computer readable medium are for segmenting different types of structures, including cancerous lesions and regular structures like vessels and skin, in a digital breast tomosynthesis (DBT) volume. In an embodiment, the method includes: pre-classification of the DBT volume in dense and fatty tissue and based on the result; localizing a set of structures in the DBT volume by using a multi-stream deep convolutional neural network; and segmenting the localized structures by calculating a probability for belonging to a specific type of structure for each voxel in the DBT volume by using a deep convolutional neural network for providing a three-dimensional probabilistic map.