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: In hemodynamic determination in medical imaging, the classifier is trained from synthetic data rather than relying on training data from other patients. A computer model (in silico) may be perturbed in many different ways to generate many different examples. The flow is calculated for each resulting example. A bench model (in vitro) may similarly be altered in many different ways. The flow is measured for each resulting example. The machine-learnt classifier uses features from medical scan data for a particular patient to estimate the blood flow based on mapping of features to flow learned from the synthetic data. Perturbations or alterations may account for therapy so that the machine-trained classifier may estimate the results of therapeutically altering a patient-specific input feature. Uncertainty may be handled by training the classifier to predict a distribution of possibilities given uncertain input distribution.

Abstract: In hemodynamic determination in medical imaging, the classifier is trained from synthetic data rather than relying on training data from other patients. A computer model (in silico) may be perturbed in many different ways to generate many different examples. The flow is calculated for each resulting example. A bench model (in vitro) may similarly be altered in many different ways. The flow is measured for each resulting example. The machine-learnt classifier uses features from medical scan data for a particular patient to estimate the blood flow based on mapping of features to flow learned from the synthetic data. Perturbations or alterations may account for therapy so that the machine-trained classifier may estimate the results of therapeutically altering a patient-specific input feature. Uncertainty may be handled by training the classifier to predict a distribution of possibilities given uncertain input distribution.

Abstract: A technique for determining a cardiac metric from rest and stress perfusion cardiac magnetic resonance (CMR) images is provided. A neural network system for determining at least one cardiac metric from CMR images comprises an input layer configured to receive at least one CMR image representative of a rest perfusion state and at least one CMR image representative of a stress perfusion state. The neural network system further comprises an output layer configured to output at least one cardiac metric based on the at least one CMR image representative of the rest perfusion state and the at least one CMR image representative of the stress perfusion state. The neural network system with interconnections between the input layer and the output layer is trained by a plurality of datasets.

Abstract: Systems and methods for determining a quantity of interest of a patient comprise receiving patient data of the patient at a first physiological state. A value of a quantity of interest of the patient at the first physiological state is determined based on the patient data. The quantity of interest represents a medical characteristic of the patient. Features are extracted from the patient data, wherein the features which are extracted are based on the quantity of interest to be determined for the patient at a second physiological state. The value of the quantity of interest of the patient at the first physiological state is mapped to a value of the quantity of interest of the patient at the second physiological state based on the extracted features.

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: Data privacy is a major concern when accessing and processing sensitive medical data. Homomorphic Encryption (HE) is one technique that preserves privacy while allowing computations to be performed on encrypted data. An encoding method enables typical HE schemes to operate on real-valued numbers of arbitrary precision and size by representing the numbers as a series of polynomial terms.

Abstract: A method includes processing at least one input dataset (using a multi-level processing algorithm, one or more of the at least one input dataset comprising imaging data of an echocardiography of a cardiovascular system of a patient. The multi-level processing algorithm comprises a multi-task level and a consolidation-task level. An input of the consolidation-task level is coupled to an output of the multi-task level. The multi-task level is configured to determine multiple diagnostic metrics of the cardiovascular system based on the at least one input dataset. The consolidation-task level is configured to determine a fitness of the cardiovascular system of the patient.

Abstract: A value indicative of an ejection fraction, EF, of a cardiac chamber of a heart is based on a temporal sequence of cardiac magnetic resonance, CMR, images of the cardiac chamber. A neural network system has an input layer configured to receive the temporal sequence of a stack of slices of the CMR images along an axis of the heart. The temporal sequence is one or multiple consecutive cardiac cycles of the heart. The neural network system has an output layer configured to output the value indicative of the EF based on the temporal sequence. The neural network system has interconnections between the input layer and the output layer and is trained with a plurality of datasets. Each of the datasets comprises an instance temporal sequence of the stack of slices of the CMR images along the axis over one or multiple consecutive cardiac cycles for the input layer and an associated instance value indicative of the EF for the output layer.

Abstract: CMR imaging is synthesized, and/or machine learning for a CMR imaging task uses synthetic sample generation. A machine-learned model generates synthetic samples. For example, the machine-learned model generates the synthetic samples in response to input of values for two or more parameters from the group of electrocardiogram (ECG), an indication of image style, a number of slices, a pathology, a measure of heart function, sample image, and/or an indication of slice position relative to anatomy. The indication of image style may be in the form of a latent representation, which may be used as the only input or one of multiple inputs. These inputs provide for better control over generation of synthetic samples, providing for greater variance and breadth of samples then used to machine train for a CMR task.

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: Systems and methods are provided for training an artificial intelligence model for detecting calcified portions of a vessel in an input medical image. One or more first medical images of a vessel in a first modality and one or more second medical image of the vessel in a second modality are received. Calcified portions of the vessel are detected in the one or more first medical images, The artificial intelligence model is trained for detecting calcified portions of the vessel in the input medical image in the second modality based on the one or more second medical images and the detected calcified portions of the vessel detected in the one or more first medical images.

Abstract: Systems and methods are provided for enhancing a medical image. An initial medical image having an initial field of view is received. An augmented medical image having an expanded field of view is generated using a trained machine learning model. The expanded field of view comprises the initial field of view and an augmentation region. The augmented medical image is output.

Abstract: Systems and methods for determining a concordance between results of medical assessments are provided. Results of a medical assessment of a first type for an anatomical object of a patient and results of a medical assessment of a second type for the anatomical object are received. The results of the medical assessment of the first type are converted to a hemodynamic measure. A concordance analysis between the results of the medical assessment of the first type and the results of the medical assessment of the second type based on the hemodynamic measure is performed. Results of the concordance analysis are output.

Abstract: Machine-based risk prediction or assistance is provided for peri-procedural complication, such as peri-procedural myocardial infarction (PMI). A machine-learned model is used to predict risk of PMI and/or recommend courses of action to avoid PMI in PCI. Various combinations of types or modes of information are used in the prediction, such as both imaging and non-imaging data. The prediction may be made prior to, during, and/or after PCI using the machine-learned model to more quickly reduce the chance of PMI. The workflows for prior, during, and/or post PCI incorporate the risk prediction and/or risk-based recommendations to reduce PMI for patients.

Abstract: Systems and methods for determining a robustness of a machine learning based medical analysis network for performing a medical analysis task on input medical data are provided. Input medical data is received. Results of a medical analysis task performed based on the input medical data using a machine learning based medical analysis network are received. A robustness of the machine learning based medical analysis network for performing the medical analysis task is determined based on the input medical data and the results of the medical analysis task using a machine learning based audit network. The determination of the robustness of the machine learning based medical analysis network is output.

Abstract: A method and system for non-invasive assessment and therapy planning for coronary artery disease from medical image data of a patient is disclosed. Geometric features representing at least a portion of a coronary artery tree of the patient are extracted from medical image data. Lesions are detected in coronary artery tree of the patient and a hemodynamic quantity of interest is computed at a plurality of points along the coronary artery tree including multiple points within the lesions based on the extracted geometric features using a machine learning model, resulting in an estimated pullback curve for the hemodynamic quantity of interest.

Abstract: Data privacy is a major concern when accessing and processing sensitive medical data. Homomorphic Encryption (HE) is one technique that preserves privacy while allowing computations to be performed on encrypted data. An encoding method enables typical HE schemes to operate on real-valued numbers of arbitrary precision and size by representing the numbers as a series of polynomial terms.

Abstract: Systems and methods for determining a concordance between results of medical assessments are provided. Results of a medical assessment of a first type for an anatomical object of a patient and results of a medical assessment of a second type for the anatomical object are received. The results of the medical assessment of the first type are converted to a hemodynamic measure. A concordance analysis between the results of the medical assessment of the first type and the results of the medical assessment of the second type based on the hemodynamic measure is performed. Results of the concordance analysis are output.