Methods and systems are provided for assessing a vasculature of an individual. In an embodiment of a method, one or more angiographic parametric imaging (API) maps of the vasculature are obtained, wherein each API map of the one or more API maps encodes a hemodynamic parameter. A state of the vasculature is determined using a machine-learning classifier applied to the one or more API maps.

A blood vessel detecting apparatus and an image-based blood vessel detecting method are provided. In the method, first to-be-evaluated data is detected through a first detecting model to obtain a first detection result. Second to-be-evaluated data is detected through a second detecting model to obtain a second detection result. The first to-be-evaluated data includes one or more medical images obtained from photographing a blood vessel. The first detection result output by the first detecting model includes one or more pixels in the medical image belonging to the blood vessel. The first detecting model and the second detecting model are constructed based on a machine learning algorithm. The second to-be-evaluated data includes the first detection result. The second detection result output by the second detecting model includes one or more pixels in the medical image belonging to the blood vessel.

Methods and systems for controlling aneurysm initiation or formation in an individual are presented; the technique comprises receiving morphological data of an artery being indicative of at least first and second geometrical parameters of the artery along its trajectory; analyzing the data to identify at least one flow-diverting location along the artery satisfying first and second predetermined conditions of the geometrical parameters; classifying the individual as having or not having disposition for future formation of an aneurysm, depending respectively on whether the at least one flow-diverting location is identified or not and generating classification data; and generating prediction data for the individual with regard to future aneurysm formation.

An example system is disclosed for generating a model of a tubular anatomical structure. The system includes an anatomical measurement wire (“AMW”), a tracking system and a computing device. The AMW is configured to be navigated through the anatomical structure of a patient, and the AMW includes at least one sensor. The tracking system is configured to provide tracking data representing multiple positions of the sensor in a spatial coordinate system. The computing device is configured to generate a data point cloud based on the tracking data, generate a parametric model corresponding to at least a portion of the vessel based on the data point cloud and store the parametric model in non-transitory memory.

Medical devices are provided, comprising a medical device and a sensor.

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.

There is provided a method for determining a regional rupture potential (RRP) indicative of the state of local weakening of a blood vessel based on parameters that correlate with the expansion and local weakening of the vessel. The method comprises: receiving a plurality of images of the blood vessel into a multiphase stack. A geometrical model of the lumen and the outer wall of the vessel are generated and smoothed to obtain a volume mesh and surface mesh respectively. An ILT thickness distribution, a local deformation at each phase and a wall strain distribution indicative of a maximal principal strain at the outer wall are determined. Blood flow values in the lumen are obtained and a wall shear stress distribution indicative of wall shear disturbances in the lumen is calculated. The RRP is determined based on the ILT thickness distribution, the wall shear stress, and the wall strain.

Medical devices are provided, comprising a medical device and a sensor.

The present disclosure relates to methods and systems for image analysis. The method may include obtaining a plurality of image sequences relating to a blood vessel. At least one of the plurality of image sequences may be acquired using a black blood imaging sequence. The method may also include determining a plurality of aligned image sequences by aligning, based on a reference image sequence of the plurality of image sequences, the plurality of image sequences, determining a target region based on the plurality of aligned image sequences, and determining one or more target parameters based on the target region.

Methods, systems, and devices for multiple wearable devices are described. A method may include receiving first physiological data associated with a user from a first wearable device worn at a first position on the user and second physiological data associated with the user from a second wearable device worn at a second position on the user. This method may include determining one or more physiological characteristics associated with the user based on a comparison of the first physiological data and the second physiological data and displaying an indication of the one or more physiological characteristics on a graphical user interface (GUI) of a user device.