A computational methodology for noninvasively assessing the severity of arterial stenosis and predicting the therapeutic outcome of interventional treatment for stenosis assessed as severe, mild, or in between based on patient's CT/MRI imaging data, ultrasound test data, and physio-pathological material properties. The method includes two major parts. The steps in the first part comprise receiving medical data, segmenting the anatomical three-dimensional geometry of the stenosed artery, setting up boundary conditions at inlet and outlets using the ultrasound velocity waveforms together with 3-element WinKessel model, and computing pulsatile pressure waveforms proximal and distal to the existing stenosis for TPI. The steps in the second part comprise of varying the VR of the stenosis virtually from 0% to 95% with an increment of 5%, computing TPI for each level of VR, establishing the functional relation between TPI and VR, identifying the two thresholds of VR.sub.mild and VR.sub.severe on TPI-VR curve, determining the severity of the existing stenosis by comparing VR.sub.existing with VR.sub.mild and VR.sub.severe concurrently and predicting the outcome of the lesion (TPI) improvement after an interventional treatment such as stenting for the existing stenosis.

A dynamic image analysis apparatus including a hardware processor that: obtains a chest dynamic image obtained by dynamic radiographing through radiation; extracts a lung field region from the dynamic image; calculates a feature amount about a blood flow rate, based on the lung field region; and limits a value of the calculated feature amount about the blood flow rate.

Methods and systems are provided for assessing obstruction of a vessel of interest of a patient, which involve obtaining a volumetric image dataset for the vessel of interest. The volumetric image dataset is analyzed to extract data representing axial trajectory of the vessel of interest. A multi-planar reformatted (MPR) image is generated from the volumetric image dataset and the data representing axial trajectory of the vessel of interest; The MPR image is supplied as input to a first machine learning network that outputs feature data that characterizes a plurality of features of the vessel of interest along the axial trajectory of the vessel of interest given the MPR image. Additional data that characterizes at least one additional feature of the vessel of interest along the axial trajectory of the vessel of interest is generated by analysis separate and distinct from the first machine learning network. The data output by the first machine learning network and the additional data is input to a second machine learning network that outputs data that characterizes anatomical lesion severity of the vessel of interest given the input data.

A method of identifying tumor treatment resistance is provided. In some embodiments, the method may include: detecting tumor oxygenated blood perfusion region inside tumor by having a patient breathe air to acquire Mill baseline data; inhalation of hyperoxia gas to generate higher than baseline HbO.sub.2 blood circulating in body to acquire MRI enhanced data; the region-of-interest (ROI), which in this case is a tumor volume (V), and which may be performed by volume contour tracing/region-of-interest (ROI) analysis and 3D tumor volumetry methods; calculating voxel's enhanced signal intensity (ΔSI); calculating tumor oxygenated perfusion percentage (OPP %); selecting different threshold and calculating maps such as a reconstruction OPP % pseudo color map; calculating tumor volume change ratio (Vt %); overlaying reconstruction OPP % pseudo color map to original images for visualizing tumor response data; drawing or plotting the OPP % and Vt % on a cancer treatment response information diagram, and identifying the type of drug resistance, classifying the drug resistance being caused by poor drug distribution factor or cells-specific factor based on pooled collected data.

A medical image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to extract a degree of a disease related to the heart from a medical image; and display, when the degree of the disease related to the heart is high, a first index value related to a blood vessel and calculated from one of blood pressure and a blood flow and configured to display, when the degree of the disease related to the heart is low, wall shear stress serving as a second index value related to the blood vessel, as information related to the blood flow of the blood vessel and calculated on the basis of the medical image.

Disclosed herein is a method of treating a subject having arterial stenosis. The method comprises: (a) providing a plurality of image frames of an artery of the subject taken in sequence; (b) in a plurality of cross-sections of the artery, determining a maximum diameter and a minimum diameter of each of the plurality of cross-sections of the artery among the plurality of image frames of the step (a); (c) calculating an average vasodilation ratio of the artery base on the maximum diameter and the minimum diameter determined in the step (b); and (d) treating the subject based on the average vasodilation ratio calculated in the step (c), by implanting a stent to the subject when the average vasodilation ratio is equal to or greater than 0.2; or administering to the subject an effective amount of a vasodilator when the average vasodilation ratio is less than 0.2.

Embodiments include systems and methods for determining cardiovascular information for a patient. A method includes receiving patient-specific data regarding a geometry of the patient's vasculature; creating an anatomic model representing at least a portion of the patient's vasculature based on the patient-specific data; and creating a computational model of a blood flow characteristic based on the anatomic model. The method also includes identifying one or more of an uncertain parameter, an uncertain clinical variable, and an uncertain geometry; modifying a probability model based on one or more of the identified uncertain parameter, uncertain clinical variable, or uncertain geometry; determining a blood flow characteristic within the patient's vasculature based on the anatomic model and the computational model of the blood flow characteristic of the patient's vasculature; and calculating, based on the probability model and the determined blood flow characteristic, a sensitivity of the determined fractional flow reserve to one or more of the identified uncertain parameter, uncertain clinical variable, or uncertain geometry.

Systems and methods for analyzing perfusion-weighted medical imaging using deep neural networks are provided. In some aspects, a method includes receiving perfusion-weighted imaging data acquired from a subject using a magnetic resonance (“MR”) imaging system and modeling at least one voxel associated with the perfusion-weighted imaging data using a four-dimensional (“4D”) convolutional neural network. The method also includes extracting spatio-temporal features for each modeled voxel and estimating at least one perfusion parameter for each modeled voxel based on the extracted spatio-temporal features. The method further includes generating a report using the at least one perfusion parameter indicating perfusion in the subject.

A computer implemented method for generating a 3D printable model of a patient specific anatomic feature from 2D medical images is provided. A 3D image is automatically generated from a set of 2D medical images. A machine learning based image segmentation technique is used to segment the generated 3D image. A 3D printable model of the patient specific anatomic feature is created from the segmented 3D image.

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.