In part, the disclosure relates to methods, and systems suitable for evaluating image data from a patient on a real time or substantially real time basis using machine learning (ML) methods and systems. Systems and methods for improving diagnostic tools for end users such as cardiologists and imaging specialists using machine learning techniques applied to specific problems associated with intravascular images that have polar representations. Further, given the use of rotating probes to obtain image data for OCT, IVUS, and other imaging data, dealing with the two coordinate systems associated therewith creates challenges. The present disclosure addresses these and numerous other challenges relating to solving the problem of quickly imaging and diagnosis a patient such that stenting and other procedures may be applied during a single session in the cath lab.

The disclosure relates to a device and a method for ascertaining at least one individual fluid-dynamic characteristic parameter of a stenosis in a vascular segment having a plurality of serial stenoses, wherein angiography image data of the vascular segment is received from an angiography recording device, geometry data of the vascular segment is ascertained by an analysis device based on the angiography image data and combined into a segment model. At least one division point located between two of the stenoses respectively is ascertained by a dividing device in the segment model, the segment model is subdivided into subsegment models at each of the at least one division points, and the respective fluid-dynamic characteristic parameter is ascertained by a simulation device for at least one of the subsegment models based on respective geometry data of the subsegment model.

A method, non-transitory computer readable medium and apparatus for estimating a pulse transmit time (PTT) to calculate a blood pressure are disclosed. For example, the method includes receiving a series of video images over a period of time that includes a first location of an individual and a second location of the individual, calculating a first set of luminance values of the first location and a second set of luminance values of the second location, estimating the PTT based on an average time difference from consecutive peaks of the first set of luminance values and the second set of luminance values and transmitting the PTT to a blood pressure calculation device to calculate the blood pressure based on the PTT that is estimated.

To acquire information relating to a vessel wall thickness by: acquiring interference signal sets of a plurality of frames including interference signal sets corresponding to a plurality of frames forming an image of the same cross section of an fundus; generating 3-D tomographic image data on the fundus from the interference signal sets of the plurality of frames; generating 3-D motion contrast data in the fundus from the interference signal sets corresponding to the plurality of frames that form the same cross section; extracting a vessel from the fundus based on the 3-D tomographic image data or the 3-D motion contrast data; detecting a coordinate of an outer surface of a vessel wall of the vessel based on the 3-D tomographic image data; and detecting a coordinate of an inner surface of the vessel wall of the vessel based on the 3-D motion contrast data.

A computer-implemented method can include generating centerlines of a patient's cardiovascular network, determining geometric features of the cardiovascular network based on the centerlines and a three-dimensional (3D) computer model of the cardiovascular network, constructing a lumped parameter network (LPN) of resistors corresponding to the cardiovascular network, and solving a system of equations corresponding to flow and pressure for the LPN model.

Devices, systems, and methods for visually depicting a vessel and evaluating a physiological condition of the vessel are disclosed. One embodiment includes obtaining, at a first time, a first image of the vessel, the image being in a first medical modality, and obtaining, at a second time subsequent to the first time, a second image of the vessel, the image being in the first medical modality. The method also includes spatially co-registering the first and second images and outputting a visual representation of the co-registered first and second images on a display. Further, the method includes determining a physiological difference between the vessel at the first time and the vessel at the second time based on the co-registered first and second images, and evaluating the physiological condition of the vessel of the patient based on the determined physiological difference.

Systems and methods are disclosed for determining anatomy directly from raw medical acquisitions using a machine learning system. One method includes obtaining raw medical acquisition data from transmission and collection of energy and particles traveling through and originating from bodies of one or more individuals; obtaining a parameterized model associated with anatomy of each of the one or more individuals; determining one or more parameters for the parameterized model, wherein the parameters are associated with the raw medical acquisition data; training a machine learning system to predict one or more values for each of the determined parameters of the parameterized model, based on the raw medical acquisition data; acquiring a medical acquisition for a selected patient; and using the trained machine learning system to determine a parameter value for a patient-specific parameterized model of the patient.

Described here are systems and methods for generating and analyzing co-expression signature data from scalar or multi-dimensional data fields contained in or otherwise derived from imaging data acquired with a medical imaging system. A similarity metric, such as an angular similarity metric, is computed between the data field components contained in pairs of voxels in the data field data. The data fields can be scalar fields, vector fields, tensor fields, or other higher-dimensional data fields. A probability distribution of these similarity metrics can be generated and used as co-expression signature data that indicate pairwise disparities in the data field data.

Systems and methods are disclosed for predicting the location, onset, or change of coronary lesions from factors like vessel geometry, physiology, and hemodynamics. One method includes: acquiring, for each of a plurality of individuals, a geometric model, blood flow characteristics, and plaque information for part of the individual's vascular system; training a machine learning algorithm based on the geometric models and blood flow characteristics for each of the plurality of individuals, and features predictive of the presence of plaque within the geometric models and blood flow characteristics of the plurality of individuals; acquiring, for a patient, a geometric model and blood flow characteristics for part of the patient's vascular system; and executing the machine learning algorithm on the patient's geometric model and blood flow characteristics to determine, based on the predictive features, plaque information of the patient for at least one point in the patient's geometric model.

An image acquisition unit acquires a phase contrast image consisting of a plurality of phases for each of three spatial directions, in which a pixel value of each pixel represents a velocity of fluid for each of the three directions, the phase contrast image being acquired by imaging a subject including a structure inside which fluid flows by a three-dimensional cine phase contrast magnetic resonance method. An identification unit identifies a region of the structure in the phase contrast image on the basis of a maximum value of the velocity of the fluid between corresponding pixels in each of the phases of the phase contrast image.