A method and system for automated correction of phase error in MRI-based flow evaluation employs a computer processor programmed to execute a trained convolutional neural network (CNN) to receive and process image data comprising flow velocity data in three directions and magnitude data collected from a region of interest over a scan period from magnetic resonance imaging instrumentation. The image data is processed using the trained CNN to generate three output channels with pixelwise inferred corrections for the flow velocity data which are further smoothed using a regression algorithm. The smoothed corrections are added to the original image data to generate corrected flow data, which may be used for flow visualization and quantization.

An ultrasonic diagnostic apparatus of an embodiment includes an ultrasonic probe and processing circuitry. The ultrasonic probe repeatedly performs scanning in which a plane wave or a diffused wave is continuously transmitted a plurality of times in the same direction in a plurality of directions. The processing circuitry performs processing of applying a moving target indicator (MTI) filter to an unequal interval data sequence in the same direction obtained by the scanning and extracting a blood flow signal in each of the plurality of directions, performs processing of generating an autocorrelation signal by performing an autocorrelation operation on a plurality of blood flow signals in the same direction for each of the directions, and estimates a velocity value of blood flow on the basis of a complex signal generated by performing complex addition of a plurality of autocorrelation signals generated for the plurality of directions.

Computer-implemented methods and systems are provided for quantitative hemodynamic flow analysis, which involves retrieving patient specific image data. A 3D reconstruction of a vessel of interest can be created from the patient specific image data. Geometric information can be extracted from the 3D reconstruction. A lesion position can be determined. Patient specific data can be obtained. Hemodynamic results can be calculated based on the geometric information, the lesion position and the patient specific data.

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.

The present invention provides an image processing method to assess quantitative myocardial blood flow and/or myocardial flow reserve, comprising the steps of: (a) pre-processing of images comprises: (i) reconstructing dynamic cine 3D tomographic myocardial perfusion imaging (MPI) data, (ii) optionally, denoising to improve the quality of image, (iii) extracting blood input function from a region of interest (ROI) of the left ventricle blood cavity, (iv) estimating the distribution volume (DV), given by the ratio of uptake and washout rates (K.sub.1/k.sub.2) to stabilize and improve estimation of K.sub.1, k.sub.2 and total blood volume (TBV) and subsequent myocardial blood flow measures, and (v) data normalization by dividing by the maximum of the blood input function; (b) assessing the individual signals pre-processed in step (a) in order to generate K.sub.1 and TBV parametric maps using artificial neural network; (c) post-processing of K.sub.1, k.sub.2 and TBV parametric maps; and of rest and stress myocardial blood flow to estimate myocardial flow reserve (MFR) and/or coronary flow reserve (CFR).

Method and systems are described that create a 3D reconstruction of a vessel of interest that represents a subset of a coronary tree that includes a lesion; calculate at least one of pressure parameters or anatomical parameters based at least in part on a portion of the 3D reconstruction that includes the lesion; calculate a wall shear stress (WSS) descriptor, based on the 3D reconstruction, for a segment of a surface of the vessel that includes the lesion, wherein the WSS descriptor includes information regarding an amount of variation in contraction or expansion applied at surface elements within the segment during at least a portion of a cardiac cycle; and calculate a myocardial infarction (MI) index based on the WSS descriptor and the at least one of the pressure or anatomical parameters, the MI index representing a likelihood that the lesion will result in an MI.

The invention relates, amongst others, to a method for determining an FFR-related parameter value, comprising: providing a CT image comprising coronary arteries obtained from coronary CT angiography, CCTA; extracting, from said CT image and for each of said coronary arteries, a respective centerline; and determining, based at least on a coronary artery model comprising said respective centerlines, said FFR-related parameter value; wherein said CT image is a 3D CT image comprising voxels, each voxel being associated with a radiodensity value, preferably a Hounsfield unit value; wherein said extracting of said respective centerlines comprises applying, on said 3D CT image comprising voxels, a first NN being a 3D NN trained with respect to the centerline; and wherein said determining of said FFR-related parameter value comprises applying, on said coronary artery model, a third NN trained with respect to FFR-related training data.

A medical image processing apparatus includes processing circuitry configured to: acquire medical image data of a site including a first blood vessel that is an artery and a second blood vessel that is an artery or a vein; specify a dominant area of the first blood vessel based on the medical image data, a blood flow strength coefficient and a damping coefficient of the first blood vessel, and a blood flow strength coefficient and a damping coefficient of the second blood vessel; and output data for displaying the specified dominant area.

Provided is a method for generating an aneurysm region including: obtaining an input image; generating a vessel mesh based on the input image; generating a vessel network including a plurality of nodes based on the vessel mesh; and performing image processing on the vessel network to generate an aneurysm region.

Provided is a method for determining a separating plane for separating aneurysm from a parent artery to obtain precise size information of an aneurysm. The method includes generating an aneurysm region including: generating a vessel mesh; generating a centerline based on the vessel mesh; generating a parent artery mesh based on the centerline; and generating a separating plane based on the parent artery mesh to obtain the aneurysm region.