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.

A route selection assistance system, a recording medium on which a route selection assistance program is recorded, a route selection assistance method, and a diagnosis method that enable easy selection of a route of a living body lumen for delivering a medical instrument to a site within a living body via the living body lumen. A route selection assistance system includes: a receiving section configured to receive an input of site information specifying a target site; an image obtaining section configured to obtain image information on a living body of a target patient; a route extracting section configured to extract a plurality of routes of a living body lumen; a ranking assigning section configured to assign rankings to the plurality of routes extracted by the route extracting section; and an output section configured to output the plurality of routes extracted and the rankings assigned by the ranking assigning section.

A method for vascular assessment is disclosed. The method, in some embodiments, comprises receiving a plurality of 2-D angiographic images of a portion of a vasculature of a subject, and processing the images to produce a stenotic model over the vasculature, the stenotic model having measurements of the vasculature at one or more locations along vessels of the vasculature. The method, in some embodiments, further comprises obtaining a flow characteristic of the stenotic model, and calculating an index indicative of vascular function, based, at least in part, on the flow characteristic in the stenotic model.

Systems and methods are disclosed for performing probabilistic segmentation in anatomical image analysis, using a computer system. One method includes receiving a plurality of images of an anatomical structure; receiving one or more geometric labels of the anatomical structure; generating a parametrized representation of the anatomical structure based on the one or more geometric labels and the received plurality of images; mapping a region of the parameterized representation to a geometric parameter of the anatomical structure; receiving an image of a patient's anatomy; and generating a probability distribution for a patient-specific segmentation boundary of the patient's anatomy, based on the mapping of the region of the parameterized representation of the anatomical structure to the geometric parameter of the anatomical structure.

A system for the detection of blood vessels includes an image sensor coupled to generate video data including a sequence of images of the blood vessels, and a heart rate monitor to measure a heart rate of a patient and to generate heart rate data. A controller is coupled to the image sensor to receive the video data, and coupled to the heart rate monitor to receive the heart rate data. The controller includes logic that when executed by the controller causes the controller to perform operations including isolate localized motion of the blood vessels in the video data using the heart rate data. The controller also computes a blood vessel mask (that includes differences between the video data and the localized motion of the blood vessels) and combined video data (that includes the video data and the blood vessel mask).

In one embodiment, a medical image processing apparatus which analyzes blood flow dynamics in a predetermined region of a subject, the blood flow dynamics being generated from medical images obtained by imaging the predetermined region in time sequence over a plurality of time phases. The medical image processing apparatus includes memory circuitry configured to store a program; and processing circuitry configured to correct pixel values of a second medical image according to an amount of deformation of the second medical image when the second medical image is aligned with a first medical image by executing the program read out from the memory circuitry, the first medical image and the second medical image being among the medical images in the plurality of time phases.

An information processing apparatus includes one or more processors, and one or more storage devices that store a program including an image generation model trained to generate, from a first image, a second image that imitates an image obtained by an imaging protocol different from an imaging protocol of the first image. The image generation model is a model trained, through machine learning using training data in which a training image captured by a first imaging protocol is associated with a correct answer clinical parameter calculated from a corresponding image captured by a second imaging protocol different from the first imaging protocol for the same subject as the training image using a modality of the same type as a modality used to capture the training image, such that a clinical parameter calculated from a generation image output by the image generation model approaches the correct answer clinical parameter.

A computer-implemented method of locating a vascular constriction in a temporal sequence of angiographic images, includes identifying (S130), from a temporal sequence of differential images, temporal sequences of a subset of sub-regions (120.sub.i,j) of the vasculature wherein contrast agent enters the sub-region, and the contrast agent subsequently leaves the sub-region; and inputting (S140) the identified temporal sequences of the subset into a neural network (130) trained to classify, from temporal sequences of angiographic images of the vasculature, a sub-region (120.sub.i,j) of the vasculature as including a vascular constriction (140).

The present disclosure relates to computing blood pressure from images of the outer eye of an individual. Images of the outer eye of an individual obtained via a high magnification camera can be analyzed using computer vision to identify features associated with blood vessels in the outer eye, such as blood vessel size or diameter, blood vessel wall thickness, distance between vessels or vessel segments, area between vessels or vessel segments, and/or blood velocity through the vessels. These blood vessel features derived from images may be used to compute a blood pressure measure(s) for an individual through use of an artificial intelligence algorithm which relates the blood vessel features to blood pressure values.

Z maps combined with a standardized stimulus in the form of a targeted arterial partial pressures of carbon dioxide provide surprisingly enhanced images for the assessment of pathological CVR. For example, the z-map assessment of patients with known steno-occlusive diseases of the cervico-cerebral vasculature showed an enhanced resolution of the presence, localization, and severity of the pathological CVR. Z-map have been found to be useful to reduce the confounding effects of test-to-test, subject-to-subject, and platform-to-platform variability for comparison of CVR images showing the importance of combining this analysis with the standardized stimulus.