A method and a system of generating a color-coded image demonstrating blood flow in a chosen area in a patient, specifically for diagnosing medical conditions like ischemia, strokes and infarcts. A plurality of images, preferably 3, are taken at different points in time that are a few seconds apart, and colorized by different colors, preferably a first image red, a second image blue and a third image green. These colorized images are then aligned and summated to create a multicolor image that time encodes perfusion of blood. Preferably, said method is performed after injection of a contrast agent.

A method for determining hemodynamic parameters may be provided. The method may include obtaining image data of a subject. The method may include generating a first vascular model and a second vascular model based on the image data and coupling the first vascular model with the second vascular model using an intermediate model to form a coupled vascular model. The method may also include setting at least one of a first boundary condition of the first vascular model or a second boundary condition of the second vascular model and determining a flow field distribution of the coupled vascular model based on the at least one of the first boundary condition or the second boundary condition. The method may further include determining hemodynamic parameters based on the flow field distribution.

The disclosure relates generally to the field of vascular system and peripheral vascular system data collection, imaging, image processing and feature detection relating thereto. In part, the disclosure more specifically relates to methods for detecting position and size of contrast cloud in an x-ray image including with respect to a sequence of x-ray images during intravascular imaging. Methods of detecting and extracting metallic wires from x-ray images are also described herein such as guidewires used in coronary procedures. Further, methods for of registering vascular trees for one or more images, such as in sequences of x-ray images, are disclosed. In part, the disclosure relates to processing, tracking and registering angiography images and elements in such images. The registration can be performed relative to images from an intravascular imaging modality such as, for example, optical coherence tomography (OCT) or intravascular ultrasound (IVUS).

An MRI image processing and analysis system may identify instances of structure in MRI flow data, e.g., coherency, derive contours and/or clinical markers based on the identified structures. The system may be remotely located from one or more MRI acquisition systems, and perform: error detection and/or correction on MRI data sets (e.g., phase error correction, phase aliasing, signal unwrapping, and/or on other artifacts); segmentation; visualization of flow (e.g., velocity, arterial versus venous flow, shunts) superimposed on anatomical structure, quantification; verification; and/or generation of patient specific 4-D flow protocols. A protected health information (PHI) service is provided which de-identifies medical study data and allows medical providers to control PHI data, and uploads the de-identified data to an analytics service provider (ASP) system. A web application is provided which merges the PHI data with the de-identified data while keeping control of the PHI data with the medical provider.

A medical image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry acquires image data including image data of a blood vessel of a subject. The processing circuitry performs analysis related to the blood vessel by using the image data, and specifies a region of interest in the blood vessel based on a result of the analysis. The processing circuitry performs fluid analysis on a region other than the region of interest at a first accuracy, and performs fluid analysis on the region of interest at a second accuracy that is higher than the first accuracy.

One or more devices, systems, methods, and storage mediums using artificial intelligence application(s) using an apparatus or system that uses and/or controls one or more imaging modalities, such as, but not limited to, angiography, Optical Coherence Tomography (OCT), Multi-modality OCT, near-infrared fluorescence (NIRAF), OCT-NIRAF, etc. are provided herein. Examples of AI applications discussed herein, include, but are not limited to, using one or more of: AI coregistration, AI marker detection, deep or machine learning, computer vision or image recognition task(s), keypoint detection, feature extraction, model training, input data preparation techniques, input mapping to the model, post-processing, and/or interpretation of output data, one or more types of machine learning models (including, but not limited to, segmentation, regression, combining or repeating regression and/or segmentation), marker detection success rates, and/or co-registration success rates to improve or optimize marker detection and/or co-registration.

Described herein is a medical system (100, 300) comprising a memory (110) storing machine executable instructions (120) and an upsampling neural network (122). The upsampling neural network is configured to output an upsampled magnetic resonance image (130) with a second resolution in response to receiving a preliminary magnetic resonance image (126) with a first resolution which is lower than the second resolution. The execution of the machine executable instructions causes a computational system (104) to: receive (200) preliminary k-space data (124); reconstruct (202) the preliminary magnetic resonance image from the preliminary k-space data; receive (204) clinical k-space data (204); receive (206) the upsampled magnetic resonance image in response to inputting the preliminary magnetic resonance image into the upsampling neural network; and provide (208) a motion corrected magnetic resonance image (132) using the upsampled magnetic resonance image and the clinical k-space data.

A system (100) for determining flow parameters of a lumen (110) in a hyperemic state induced subsequent to a contrast agent injection (Inj 1) into the lumen in a basal state, is provided. The system comprising one or more processors (120) configured to: determine (S110), based on received angiographic data representing the injected contrast agent and/or received injector data representing the injected contrast agent, a temporal window (TH.sub.0, TH.sub.1) representing a duration of the induced hyperemic state; and output (S120) a signal (Sh) indicative of the temporal window (TH.sub.0, TH.sub.1).

The present disclosure provides a computer-implemented method which comprises a first step of obtaining an image data of the body portion from an ultrasound probe; a second step of analyzing the image data by use of machine learning and identifying a target and an evasion object; and a third step of determining the location of a final target, with reference to the information of the identified target and the evasion object. The final target is determined by at least one of a first information relating to the size of the target, a second information relating to the depth of the target, and a third information relating to the existence of an object in the straight path between the target and skin surface.

According to one embodiment, a medical image processing apparatus includes first specifier, second specifier, determiner and display controller. First specifier collates an ischemic region calculated from a blood vessel visualized into a three-dimensional image in a plurality of phases with a dominating region of the blood vessel, and specifies a culprit vessel in the ischemic region. Second specifier specifies a culprit stenosis in the culprit vessel based on a pressure index calculated from the blood vessel. Determiner determines a connection position to connect a bypass vessel that makes a detour around the culprit stenosis. Display controller displays the determined connection position on a display.