Methods and systems for characterizing tissue of a subject are disclosed. The method includes retrieving a time series of angiography images of tissue of a subject, defining a plurality of calculation regions, generating a time-intensity curve for each respective calculation region, calculating a rank value for each respective calculation region based on one or more parameters derived from the time-intensity curve; and generating a viewable image in which on the image position of each calculation region an indication is provided of the calculated rank value for that calculation region. Also disclosed are methods and systems for generating first and second time-intensity curves for respective first and second calculation regions, calculating first and second rank values for the respective calculation regions based on first and second pluralities of parameters selected to approximate the respective time-intensity curves, and generating a spatial map of the first and second calculated rank values.

A method for determining respiratory induced blood mass change from a four-dimensional computed tomography (4D CT) includes receiving a 4D CT image set which contains a first three-dimensional computed tomographic image (3D CT) and a second 3D CT image. The method includes executing a deformable image registration (DIR) function on the received 4D CT image set, and determining a displacement vector field indicative of the lung motion induced by patient respiration. The method further includes segmenting the received 3D CT images into a first segmented image and a second segmented. The method includes determining the change in blood mass between the first 3D CT image and the second 3D CT image from the DIR solution, the segmented images, and measured CT densities.

An ultrasound image recognition system and a data output module are provided. The ultrasound image recognition system includes an image analyzing device, a data processing device, and a data output module. The image analyzing device is configured to receive an image having a predetermined format. The image analyzing device generates a plurality of physiological image parameters. The data processing device is connected to the data processing device. The image analyzing device provides the plurality of physiological image parameters and the image having a predetermined format to the data processing device. The data processing device provides a comparison result of physiological parameter based on the plurality of physiological image parameters and a plurality of predetermined physiological image parameters. The data processing device converts the image having a predetermined format into an image having a first format.

Systems and methods are disclosed for correcting for artificial deformations in anatomical modeling. One method includes obtaining an anatomic model; obtaining information indicating a presence of an artificial deformation of the anatomic model; identifying a portion of the anatomic model associated with the artificial deformation; estimating a non-deformed local area corresponding to the portion of the anatomic model; and modifying the portion of the anatomic model associated with the artificial deformation, based on the estimated non-deformed local area.

This application discloses a method, a computer device, a storage medium, and a program product for obtaining the Index of Microvascular Resistance (IMR). The method comprises the following steps: obtaining the resting pressure of the coronary artery ostium, and calculating the cardiac output pressure based on the resting pressure of the coronary artery ostium. Obtaining the hyperemic pressure of the coronary artery ostium, and obtaining the coronary flow reserve based on the cardiac output pressure, the resting pressure, and the hyperemic pressure. Obtaining the medical images of the coronary system, obtaining the three-dimensional model of the target vessel, and calculating the morphological parameters of the coronary artery based on the three-dimensional model of the target vessel. Calculating the resting blood flow and the flow resistance based on the morphological parameters.

Systems and methods are disclosed for predicting coronary plaque vulnerability, using a computer system. One method includes acquiring anatomical image data of at least part of the patient's vascular system; performing, using a processor, one or more image characteristics analysis, geometrical analysis, computational fluid dynamics analysis, and structural mechanics analysis on the anatomical image data; predicting, using the processor, a coronary plaque vulnerability present in the patient's vascular system, wherein predicting the coronary plaque vulnerability includes calculating an adverse plaque characteristic based on results of the one or more of image characteristics analysis, geometrical analysis, computational fluid dynamics analysis, and structural mechanics analysis of the anatomical image data; and reporting, using the processor, the calculated adverse plaque characteristic.

Systems and methods are disclosed for predicting the location, onset, or change of coronary lesions from factors like vessel geometry, physiology, and hemodynamics. One method includes: acquiring, for each of a plurality of individuals, a geometric model, blood flow characteristics, and plaque information for part of the individual's vascular system; training a machine learning algorithm based on the geometric models and blood flow characteristics for each of the plurality of individuals, and features predictive of the presence of plaque within the geometric models and blood flow characteristics of the plurality of individuals; acquiring, for a patient, a geometric model and blood flow characteristics for part of the patient's vascular system; and executing the machine learning algorithm on the patient's geometric model and blood flow characteristics to determine, based on the predictive features, plaque information of the patient for at least one point in the patient's geometric model.

An image processing apparatus includes an image acquisition unit acquiring a medical image, a region setting unit setting a peripheral region around an inner region set in the medical image as a region including a lesion, an intensity value ratio distribution calculation unit calculating a histogram comprising a distribution of intensity value ratios for the inner region and calculating a histogram being a distribution of intensity value ratios for the peripheral region, a ratio difference calculation unit that calculates a ratio difference comprising a difference between intensity value ratios in the inner region and peripheral regions for each of predetermined intensity values, an intensity value determination unit selecting a pixel to be highlighted in the medical image based on the ratio difference, and a display processing unit outputting the medical image whereby the pixel selected by the pixel selection unit is highlighted in the medical image to a display device.

A system and method is provided for assessing efficacy of placement of a vascular implant medical device that has been implanted in a subject. The method includes accessing, with a computer system, image data acquired from a subject using a medical imaging system. The image data include at least one image of the vascular implant device implanted within a vascular structure of the subject after the subject has received an injection of a contrast agent at a contrast injection site. The method also includes determining, from the image data, a region of interest (ROI) that includes the vascular structure and is downstream of the contrast injection site and developing a contrast model from the image data. The method further includes, using the contrast model, determining a flow time constant and, using the flow time constant, assessing an efficacy of the vascular implant device implanted in the vascular structure.

The present disclosure relates generally to medical imaging, and more specifically to extracting a subset of images from a series of images (e.g., surgical video feeds) for training machine-learning models and/or conducting various downstream analyses. The system can hash image data for each image of a series of video images of the surgery to obtain a series of hash values; calculate a plurality of difference values for the series of hash values, each of the plurality of difference values indicative of a difference between two consecutive hash values in the series of hash values; generate a plurality of image clusters by clustering the plurality of distance values; select one or more image clusters from the plurality of image clusters; and produce a subset of surgical images from the series of video images using the selected one or more image clusters from the plurality of image clusters.