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.

A method for quantitative flow analysis of a fluid flowing in a conduit from a sequence of consecutive image frames of such a conduit, where such image frames are timely separated by a certain time interval, the method comprising: a) selecting a start image frame and an end image frame from the sequence either automatically or upon user input; b) determining a centerline of the conduit in the start image frame; c) determining a centerline of the conduit in the end image frame; d) selecting a common start point on the centerline of the start image frame and on the centerline of the end image frame either automatically or upon user input; e) selecting an end point on the centerline of the start image frame; f) selecting an end point on the centerline of the end image frame; g) calculating centerline distance between the start point and the end point of the start image frame; h) calculating centerline distance between the start point and the end point of the end image frame; and i) calculating a local flow velocity as a function of the centerline distances of g) and h) and a time interval between the start image frame and the end image frame.
A corresponding imaging device and computer program are also disclosed.

A system for displaying medical imaging data comprising one or more data inputs, one or more processors, and one or more displays, wherein the one or more data inputs are configured for receiving first image data generated by a first medical imaging device, wherein the first image data comprises a field of view (FOV) portion and a non-FOV portion, and the one or more processors are configured for identifying the non-FOV portion of the first image data and generating cropped first image data by removing at least a portion of the non-FOV portion of the first image data, and transmitting the cropped first image data for display in a first portion of the display and additional information for display in a second portion of the display.

A method of treating a human patient identified as having an injury to a body region includes receiving CT images, where the CT images were generated by performing a CT angiography of an injured body region of the human patient. The method further includes determining, based on the CT images, a total volumetric rate of active bleeding in the injured body region; and recommending at least one treatment approach for the human patient based on the total volumetric rate of active bleeding.

Methods and systems are provided for adaptive scan control. In one embodiment, a method includes upon a first contrast injection, processing acquired projection data of a subject to measure a contrast signal of the first contrast injection, estimating a time when a venous return to baseline (VRTB) of the first contrast injection is to occur based on the contrast signal, and commanding initiation of a second contrast injection at the estimated time.

A medical image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to extract a degree of a disease related to the heart from a medical image; and display, when the degree of the disease related to the heart is high, a first index value related to a blood vessel and calculated from one of blood pressure and a blood flow and configured to display, when the degree of the disease related to the heart is low, wall shear stress serving as a second index value related to the blood vessel, as information related to the blood flow of the blood vessel and calculated on the basis of the medical image.

A method for determining hemodynamic parameters may be provided. The method may include obtaining image data of a subject. The method may include generating a first vascular model and a second vascular model based on the image data and coupling the first vascular model with the second vascular model using an intermediate model to form a coupled vascular model. The method may also include setting at least one of a first boundary condition of the first vascular model or a second boundary condition of the second vascular model and determining a flow field distribution of the coupled vascular model based on the at least one of the first boundary condition or the second boundary condition. The method may further include determining hemodynamic parameters based on the flow field distribution.

Provided herein are systems, methods, and media capable of determining estimated force applied on a target tissue region to enable tactile feedback during interaction with said target tissue region.

The invention provides a technique capable of effectively and appropriately removing noise from various kinds of images including noise and artifacts and images in which a noise pattern changes due to a difference in imaging conditions. Based on a noise removal technique using AI, noise characteristics including artifacts are analyzed for each image, the image is classified based on an analysis result, an optimal neural network for a noise processing is applied for each classification, and the noise and the artifacts are reduced.

The invention provides an ultrasound imaging apparatus capable of highly accurately extracting a blood flow in a fine blood vessel in a short time. N pieces of frame data is generated by receiving ultrasound waves reflected by a subject with a plurality of transducers. A correlation matrix is generated based on a vector in which data at a corresponding position of the frame data is arranged for N frames, and a singular value and a singular vector for each of N ranks are calculated. A first filter element is calculated based on a variance between data at a corresponding position zx among a plurality of blood flow component frame data obtained by multiplying a plurality of the frame data by singular vectors at a threshold rank k or more. The second filter element is calculated based on the tissue component frame data obtained by multiplying the frame data by a singular vector at a rank 1. The frame data is weighted by the first filter element and/or the second filter element to generate a clutter reducing image.