Automated cerebral microbleed detection is performed in extracted T2*-weighted image data, including gradient echo (GRE) image data and susceptibility-weighted imaging (SWI) image data. The image data is resampled and potential 2D regions of interest (ROI) having a circular or ellipsoidal shape are identified based in part on a respective intensity of associated resampled image pixels. The number of 2D ROIs are reduced by size, edge, and/or cerebrospinal fluid (CSF) mask exclusion, and then merged to form 3D ROIs. False positive 3D ROIs are removed and the remaining ROIs stored for review by a trained rater. The embodiments of the present disclosure outperform visual ratings of cerebral microbleeds, reducing the time to visually rate the scans while retaining sensitivity to the microbleeds themselves. These embodiments also exhibit higher sensitivity in longitudinal identification of microbleed locations, and are suited to longitudinal examination of cerebrovascular disease, e.g., Alzheimer’s in adults with Down syndrome.

A method of detecting a flow in sequence of images of a material. Providing a sequence of at least three images of an area of the material. Each image includes a plurality of voxels or regions of interest such that the at least three images of the area of the material provide for each voxel or region of interest an intensity for at least three points in time, Fourier transforming for each voxel or region of interest to obtain a frequency distribution including the intensities for the at least three points in time, analysing for each voxel or region of interest and generating a processed image of area of the material including the voxels or regions of interest, associating voxels or regions of interest that have a larger amplitude at a higher frequency range with a first visual property and voxels or regions of interest that have smaller amplitude in the higher frequency range with a second visual property.

Systems and methods for determining corresponding locations of points of interest in a plurality of input medical images are provided. A plurality of input medical images comprising a first input medical image and one or more additional input medical images is received. The first input medical image identifies a location of a point of interest. A set of features is extracted from each of the plurality of input medical images. Features between each of the sets of features are related using a machine learning based relational network. A location of the point of interest in each of the one or more additional input medical images that corresponds to the location of the point of interest in the first input medical image is identified based on the related features. The location of the point of interest in each of the one or more additional input medical images is output.

A dynamic image analysis apparatus includes a hardware processor that acquires an X-ray dynamic image including continuous frame images acquired by continuously capturing a living body having a heartbeat in time series; performs logarithmic conversion for a pixel value of the acquired X-ray dynamic image to create a logarithmically converted image; sets, as a reference frame image, one frame image based on a heartbeat phase in at least one of the X-ray dynamic image and the logarithmically converted image; calculates (i) a difference or ratio between the X-ray dynamic image as the reference frame image and the X-ray dynamic image as a comparative frame image which is another frame image or (ii) a difference or ratio between the logarithmically converted image as the reference frame image and the logarithmically converted image as the comparative frame image; and generates a blood flow analysis image.

There is provided a method for registration of intravital anatomical imaging modality image data and nuclear medicine image data of a patient's heart comprising: obtaining anatomical image data including a heart of a patient outputted by an anatomical intravital imaging modality; obtaining at least one nuclear medicine image data outputted by a nuclear medicine imaging modality, the nuclear medicine image data including the heart of the patient; identifying a segmentation of a network of vessels of the heart in the anatomical image data; identifying a contour of at least part of the heart in the nuclear medicine image data, the contour including at least one muscle wall border of the heart; correlating between the segmentation and the contour; registering the correlated segmentation and the correlated contour to form a registered image of the anatomical image data and the nuclear medicine image data; and providing the registered image for display.

A computer implemented method for assessing an arterio-venous malformation (AVM) may include, for example, receiving a patient-specific model of a portion of an anatomy of a patient; using a computer processor to analyze the patient-specific model for identifying one or more blood vessels associated with the AVM, in the patient-specific model; and estimating a risk of an undesirable outcome caused by the AVM, by performing computer simulations of blood flow through the one or more blood vessels associated with the AVM in the patient-specific model.

Disclosed is a method for calculating an instantaneous wave-free ratio based on a pressure sensor and angiogram images, comprising: acquiring pressures at the coronary artery ostium of heart by a blood pressure sensor in real-time, and storing the pressure values in a data linked table, and the data linked table being indexed by time and the time and real-time pressure being saved in the form of key-value pairs; finding out corresponding datas from the data queue based on time index using the angiography time as an index value, taking an average value of four wave-free pressure values as a wave-free pressure value Pa; obtaining a time Tn of an end phase of a diastolic period, namely of a wave-free period within one cycle according to the time index within the cycle; obtaining a length L of a segment of a blood vessel through angiogram images of two body positions, and obtaining a blood flow velocity V; calculating a pressure drop ΔP and calculating a pressure Pd which at the distal end of the blood vessel as Pd=Pa−ΔP, and further obtaining the instantaneous wave-free ratio.

A smartphone-based hemoglobin (Hgb) assessment application quantitatively analyzes pallor in patient-sourced photos using image analysis algorithms to enable a noninvasive, accurate quantitative smartphone app for detecting anemia. A user takes a photo of his/her fingernail beds using the app and receives an accurate displayed Hgb level. Since fingernails do not contain melanocytes, the primary source of color of these anatomical features is blood Hgb. At the same time, quality control software minimizes the impact of common fingernail irregularities (e.g. leukonychia and camera flash reflection) on Hgb level measurement. Metadata recorded upon capturing the image is leveraged for determining a users' Hgb level thereby eliminating the need for external equipment. A personalized calibration of image data with measured Hgb levels improves the accuracy of the application.

A system and method include identification of a blood pool region of the body based on the acquired positron emission tomography data, determination of activity in the blood pool region associated with a first time based on the acquired positron emission tomography data, determination of a scale factor based on the determined activity in the blood pool region and on an input function associated with one or more other human bodies, determination of activity in a tissue region associated with a second time based on the acquired positron emission tomography data, determination of a specific uptake ratio associated with the second time based on the activity in the tissue region, the scale factor and a value of the input function at the second time, determination of an estimate of metabolic rate in the tissue region based on the specific uptake ratio and the input function, and determination of a diagnosis associated with the tissue region based on the estimate of metabolic rate.

An apparatus for determining a functional index for stenosis assessment of a vessel is provided. The apparatus comprises an input interface (40) and a processing unit (50). The input interface is configured to obtain image data (30) representing a two-dimensional representation of a vessel (6). The processing unit (50) is configured to determine a course of the vessel (6) and a width (w1, w2) of the vessel along its course in the image data and is further configured to determine the functional index for stenosis assessment of the vessel based on the width of the vessel in the image data.