Blood coagulation testing can be performed by measuring, based on video of a blood sample flowing in a microfluidic channel, the time it takes until flow stops due to clotting. In various embodiments, such measurements are enabled by a low-cost testing system that includes a microfluidic cartridge and uses a smartphone or similar device for video acquisition, in conjunction with a lighting module for illuminating the microfluidic channel and a 3D-printed platform for holding and positioning and orienting the cartridge, lighting module, and smartphone in fixed special relation to each other.

A system is described for generating a revascularization score for a blockage of a coronary artery in a heart. The system accesses indications of viability of myocardial tissue in the heart, a blockage state of the blockage that includes a blockage location and a blockage amount, and the perfusion territory of the myocardial tissue. Based on the myocardial tissue state, blockage state, and perfusion territory, the system generates a revascularization score for the blockage. The system generates a graphic of the heart that illustrates coronary arteries, myocardial tissue state, blockage state, and the revascularization score. The system displays the graphic to provide a visual representation of the revascularization score for the blockage of the coronary artery.

An analysis method is provided that ensures objective and quantitative analysis for analyzing time-series images. For implementing the method are provided an image data storage unit that stores therein image data on a plurality of time-series computed tomography (CT) images of an organ of a subject captured after a contrast medium has been administered; a target pixel extraction unit configured to extract an intra-organ pixel position, which is a position of a pixel in a region of the organ; a change-over-time determining unit configured to determine a change-over-time of a CT value of the pixel at the determined intra-organ pixel position, based on image data on the time series CT images in the plurality of frames; and a function approximation processing unit configured to determine an arrival time at which the contrast medium has arrived at an organ at the intra-organ pixel position and a base value, which is a CT value serving as a base of the pixel at the intra-organ pixel position, based on the determined change-over-time.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

A method and system for determining fractional flow reserve (FFR) for a coronary artery stenosis of a patient is disclosed. In one embodiment, medical image data of the patient including the stenosis is received, a set of features for the stenosis is extracted from the medical image data of the patient, and an FFR value for the stenosis is determined based on the extracted set of features using a trained machine-learning based mapping. In another embodiment, a medical image of the patient including the stenosis of interest is received, image patches corresponding to the stenosis of interest and a coronary tree of the patient are detected, an FFR value for the stenosis of interest is determined using a trained deep neural network regressor applied directly to the detected image patches.

In spectral Doppler imaging, a high PRF is used independent of the velocity scale. The adjustment is then of the velocity scale. By optimizing the velocity scale independent of the high PRF in an on-going or automated basis, user activation may be avoided and/or interruption to reconfigure for an altered PRF may be avoided. The acquired data may be stored, allowing for past data to be processed again when a new velocity scale or other setting is selected. The resulting spectral Doppler image may continue to display spectra over time without a gap or without premature loss of spectra due to reconfiguring.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

Methods for personalized treatment of tumor lesions in subject with metastatic cancer are disclosed.

An image processing apparatus comprising an image producing unit 101 for producing an axial image of a body part to be imaged including an aorta and an esophagus; a map generating unit 102 for generating a map M2 for locating a region in which a probability that the aorta lies is high in the axial image; a detecting unit 103 for detecting a temporary position of the aorta based on the map M2; and a deciding unit 104 for making a decision on whether or not the temporary position of the aorta falls within the region of the aorta in the axial image based on a distribution model DM containing information representing a reference position (x.sub.e, y.sub.e) of the esophagus and information representing a range over which the aorta distributes relative to the reference position (x.sub.e, y.sub.e) of the esophagus, and on the map M2.