Systems and methods for guiding a blood flow restoration procedure (e.g., thrombectomy) are disclosed herein. In some variations in accordance with the present technology, a method for guiding removal of a thrombus includes receiving image data indicative of positions of marker elements on a thrombectomy device positioned proximate the thrombus in a patient, and predicting at least one characteristic of the thrombus by applying a trained machine learning algorithm to the image data.

Blood flow beneath a user's skin, for example, in a user's face may be visually rendered. In some aspects, a plurality of differences is determined in the intensity of pixels of a first image and the corresponding pixels of a subsequent second image. In some aspects, this plurality of differences is enhanced to accentuate a characteristic associated with the first image and the second image. The enhanced plurality of differences is visually rendered for each subsequent comparison of pixel intensity values.

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.

Method for assessing a regurgitant flow through a valve into a moving object from a sequence of consecutive image frames of such object, which images are timely separated by a certain time interval, the method comprising the following steps: a) identifying in the images the object of interest; b) augmenting the images to compensate for protocol intensity variation and/or motion and/or background; c) making a time-analysis of augmented images to obtain time-density curve or curves, wherein time-density curves represent a time-evolution of pixel brightness; d) determining a plurality of parameters related to such time-density curve or curves; e) weighting such parameters to provide indications on the regurgitant flow. A corresponding apparatus and computer program are also disclosed.

A method and system for computing blood flow in coronary arteries from medical image data disclosed. Patient-specific anatomical measurements of a coronary artery tree are extracted from medical image data of a patient. A reference radius is estimated for each of a plurality of branches in the coronary artery tree from the patient-specific anatomical measurements of the coronary artery tree. A flow rate is calculated based on the reference radius for each of the plurality of branches of the coronary artery tree. A plurality of total flow rate estimates for the coronary artery tree are calculated. Each total flow rate estimate is calculated from the flow rates of branches of particular generation in the coronary artery tree. A total flow rate of the coronary artery tree is calculated based on the plurality of total flow rate estimates. The total flow rate of the coronary artery tree can be used to derive boundary conditions for simulating blood flow in the coronary artery tree.

A system and method for analysis of a vessel automatically detects a pathology in a first image of the vessel and attaches a virtual mark to the pathology in the first image. The system may detect the same pathology in a second image of the vessel, based on the virtual mark, and may then provide analysis (e.g., determine an FFR value) of the pathology based on the pathology detected in the first and second images.

An image processing apparatus according to an embodiment includes an input extraction unit, and a parameter estimation unit. The input extraction unit configured to extract, from time sequence images of medical images obtained by performing a blood perfusion imaging scan on a tissue, a region where a main vessel supplying blood input to the tissue is located as an input part and obtain, based on the input part, a time-density sequence of the blood input as an input time sequence. The parameter estimation unit configured to estimate parameters in a model representing the change of the blood flow at each point in the tissue with the input time sequence, based on the input time sequence, the model and the time sequence images, wherein the parameters include a delay undergone by the blood when flowing from the input part to each point in the tissue.

Embodiments disclose systems and methods that aid in screening, diagnosis and/or monitoring of medical conditions. The systems and methods may allow, for example, for automated identification and localization of lesions and other anatomical structures from medical data obtained from medical imaging devices, computation of image-based biomarkers including quantification of dynamics of lesions, and/or integration with telemedicine services, programs, or software.

Embodiments disclose systems and methods that aid in screening, diagnosis and/or monitoring of medical conditions. The systems and methods may allow, for example, for automated identification and localization of lesions and other anatomical structures from medical data obtained from medical imaging devices, computation of image-based biomarkers including quantification of dynamics of lesions, and/or integration with telemedicine services, programs, or software.

Embodiments include methods and systems and for determining a sensitivity of a patient's blood flow characteristic to anatomical or geometrical uncertainty. For each of one or more of individuals, a sensitivity of a blood flow characteristic may be obtained for one or more uncertain parameters. An algorithm may be trained based on the sensitivities of the blood flow characteristic and one or more of the uncertain parameters for each of the plurality of individuals. A geometric model, a blood flow characteristic, and one or more of the uncertain parameters of at least part of the patient's vascular system may be obtained for a patient. The sensitivity of the patient's blood flow characteristic to one or more of the uncertain parameters may be calculated by executing the algorithm on the blood flow characteristic of at least part of the patient's vascular system, and one or more of the uncertain parameters.