Various embodiments described herein relate to systems, devices, and methods for non-invasive image-based plaque analysis and risk determination. In particular, in some embodiments, the systems, devices, and methods described herein are related to analysis of one or more regions of plaque, such as for example coronary plaque, using non-invasively obtained images that can be analyzed using computer vision or machine learning to identify, diagnose, characterize, treat and/or track coronary artery disease.

An apparatus for analyzing a vasculature of a patient and a corresponding method are provided in which a plurality of simulated hemodynamic parameter values obtained from (non-invasively) acquired diagnostic images are compared to at least one intravascular hemodynamic parameter value acquired during an invasive measurement in a vessel of interest in the vasculature. The comparison allows to uniquely determine the vessel of interest. Based on this information, the assessment of the disease and the potential treatment planning may be improved.

A radiation diagnosis apparatus according to an embodiment includes reconstructing circuitry. The reconstructing circuitry reconstructs three-dimensional medical agent distribution images in a time series from a group of acquired images acquired in the presence of a medical agent by an imaging system from directions in a range that makes it possible to reconstruct three-dimensional images of a subject, by performing an iterative reconstruction process that uses at least one selected from between spatial continuity of the medical agent and temporal continuity of the concentration of the medical agent as a constraint condition.

There are provided a medical image processing apparatus, an endoscope apparatus, a diagnostic support apparatus, and a medical service support apparatus capable of detecting red blood cells using an endoscope image. A medical image processing apparatus includes: a medical image acquisition unit that acquires short wavelength medical images, which are medical images including a subject image and which are obtained by imaging a subject with light in a shorter wavelength band than a green wavelength band; and a red blood cell detection unit that detects red blood cells using the short wavelength medical images. The light in the short wavelength band is, for example, light in a blue band or a violet band of a visible range. The red blood cell detection unit detects, for example, a high-frequency, granular, and high-density region as red blood cells.

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: 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. An asynchronous command and imaging pipeline allows remote image processing and analysis in a timely and secure manner even with complicated or large 4-D flow MRI data sets.

Disclosed herein is a method comprising a method comprising imaging a network section through which flow occurs; where the flow is selected from a group consisting of fluid, electrons, protons, neutrons and holes; partitioning the image into sub-regions based on metabolic need and function; where each region comprises one or more sources and one or more sinks; where the flow emanates from the source and exits into the sinks; generating a Voronoi diagram from the Delaunay triangulation by subdividing the sub-regions into Voronoi cells, where each Voronoi cell contains exactly one sink or one source; and where the intersections of Voronoi cells are Voronoi cell vertices; calculating a flow rate in each Voronoi cell; and according a color to Voronoi cells based on their flow rates; where Voronoi cells having similar rates are accorded similar colors.

Disclosed herein is a method comprising imaging a network section through which flow occurs; where the flow is selected from a group consisting of fluid, electrons, protons, neutrons and holes; partitioning the image into sub-regions based on metabolic need and function; where each region comprises one or more sources and one or more sinks; where the flow emanates from the source and exits into the sinks; performing a Delaunay triangulation tessellation on one or more sub-regions by connecting one or more sources and one or more sinks; where the Delaunay triangulations maximize the minimum angle of all the angles of the triangles in the triangulation; generating a Voronoi diagram from the Delaunay triangulation by subdividing the sub-regions into Voronoi cells, where each Voronoi cell contains exactly one sink or one source; and where the intersections of Voronoi cells are Voronoi cell vertices; locating a sink endpoint centroid; connecting a source to a nearest Voronoi cell vertex; connecting at least one sink to at least one of the remaining Voronoi cell vortices to complete the network; and performing a smoothing function on the network to form a smoothed network.

The present invention relates to the field of medical imaging in the absence of contrast agents. In one form, the invention relates to the field of imaging vessels, particularly blood vessels such as the pulmonary vasculature and is suitable for use as a technique for detecting pulmonary embolism (PE), such as acute PE. Embodiments of the present invention provide improved image processing techniques having the capability to extract and use image data to overcome the need for contrast agents to distinguish between different types of tissue. Furthermore, it has also been realised that the image data accessed by the improved image processing can be used to identify irregularities in vessels.

In an embodiment, a method converts two images to a transform representation in a transform domain. For each spatial position, the method examines coefficients representing a neighborhood of the spatial position that is spatially the same across each of the two images. The method calculates a first vector in the transform domain based on first coefficients representing the spatial position, the first vector representing change from a first to second image of the two images describing deformation. The method modifies the first vector to create a second vector in the transform domain representing amplified movement at the spatial position between the first and second images. The method calculates second coefficients based on the second vector of the transform domain. From the second coefficients, the method generates an output image showing motion amplified according to the second vector for each spatial position between the first and second images.

A method of determining a residue function in brain tissue, from medical images acquired after introducing contrast agent into the blood, correcting for contrast agent leakage into the tissue, comprising: a) providing time signals indicating contrast agent concentration for leaking voxels, a time signal indicating average contrast agent concentration for non-leaking voxels, and an artery input function, all derived from the images; b) fitting the leaking voxel signals to a model time signal with a free parameter for leakage rate, the model assuming that the concentration of contrast agent perfusing through a leaking voxel has a same shape as a function of time as the average contrast agent concentration for non-leaking voxels; c) using the best fit leakage rate parameter to make a correction for leakage to the leaking voxel signals; and d) deconvolving the corrected signals from the artery input function, to find the residue function.