The present disclosure includes an ultrasonic diagnostic device including one or more processors and/or circuitry configured to execute first acquisition processing of acquiring, based on an ultrasonic signal reflected in a living body, measurement data including tissue-derived information and blood-flow-derived information, execute second acquisition processing of acquiring, based on blood flow data obtained by extracting or emphasizing the blood-flow-derived information of the measurement data, a deformation amount between a plurality of frames, and execute generation processing of generating display image data by aligning feature points in the blood-flow-derived information between the frames based on the deformation amount and then synthesizing the aligned feature points for the plurality of frames.

Whether to deploy a cerebral embolic protection (CEP) device is determined by initially retrieving one or more image of at least part of an aorta. The image includes an aortic valve. The image is segmented to identify the aortic valve, an aortic arch, and a plurality of branching blood vessels downstream of the aortic valve. Plaque in a segment of the image at or adjacent the aortic valve is identified, and a vulnerability score associated with the plaque is generated. Dynamics of blood flow in the aortic arch and at least one of the plurality of branching blood vessels is evaluated. It is then determined whether a CEP device should be deployed at least partially based on the vulnerability score. A CEP device is selected at least partially based on the dynamics of blood flow in the aortic arch.

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.

For segmentation and/or ejection fraction determination, photon-counting detection for CECT in ES is used to create virtual NCCT for machine training. The lack of NCCT data at ES for training segmentation is overcome by use of the virtual NCCT. Since the segmentation is trained on non-contrast imaging at ES, accurate ES segmentation from NCCT may be provided. The EF may be calculated using NCCT.

A dynamic image processing apparatus includes a hardware processor. The hardware processor performs the following, acquiring a dynamic image including a plurality of frame images obtained by dynamic imaging using radiation, setting a certain region of interest in each of the plurality of frame images that are acquired, and generating waveform information indicating a change in a signal value of each pixel in the set region of interest of the plurality of frame images. The hardware processor tracks the region of interest set in a standard frame image among the plurality of frame images with respect to another frame image to set the region of interest in the another frame image.

There is provided a system for determining a pulse transit time (PTT) of a subject. The system includes at least one light source to generate laser light illuminating at least one region of the subject, forming a speckle pattern on the region; at least one imaging detector to acquire images of the speckle pattern; and a processing unit to process the images. The light source and the imaging detector generate images representing speckle vibrometry (SV) data and images representing speckle plethysmography (SPG) data. The processing unit extracts the SV data from the images and processes the SV data for determining a first time point representing a flow of blood entering an aorta. The processing unit extracts and processes the SPG data for determining a second time point representing a blood pulse arrival in a location of an artery system associated with the region. The processing unit determines the PTT based on the first and second time points.

A method for determining a measure of disease in a region of the body of a subject. The method receives imaging data from imaging the region of the body of the subject having data representative of a distribution of a marker within the region administered to the subject prior to the imaging. The marker binds to a biological target related to the disease. The imaging data is processed to obtain a measure of marker signal in the region. The measure of marker signal is corrected for an effect on the marker signal of tissue structure within the region. The measure of the disease is determined for the region using the corrected measure of marker signal. Also provided is an apparatus configured to carry out the method.

A method for refiguring an ultrasound dose and systems relating to same. The method may involve creating a medium property map of a region of interest of a subject, wherein the medium property map provides a plurality of different medium property values in different segments of the region of interest dependent on the medium within each of said segments. The method may further involve obtaining an image of the region of interest, wherein the region of interest comprises a target treatment area and a surrounding region of the target treatment area; processing the image to identify different components of the region of interest, segmenting and categorizing the different components into predetermined media categories, and retrieving a medium property value associated with each media category and attributing said medium property value to each respective component of the segmented region of interest.

A computer-implemented method of predicting a status of an embolization procedure on an aneurism, includes: receiving (S110) projection image data (110) representing temporal blood flow in a region of the anatomy including the aneurism during the embolization procedure; inputting (S120) the received projection image data (110) into a neural network (120) trained to predict temporal blood flow (130), wherein the neural network (120) is trained to predict temporal blood flow (130) using training data (140) representing temporal blood flow in a region of the anatomy that does not include an aneurism; and in response to the inputting (S120): generating (S130) an output (160) indicative of the status of the embolization procedure based on the predicted temporal blood flow.

A volume management apparatus includes a cardiac evaluation unit configured to evaluate whether the patient suffers from an organic heart disease based on an image of apical four chamber view; a parameter determination unit configured to determine a maximum inner diameter of inferior vena cava and a collapse rate of inferior vena cava based on an image of subxiphoid inferior vena cava view; a score calculation unit configured to determine scores that correspond to the LVEF value, the maximum diameter of the inferior vena cava, and the collapse rate of the inferior vena cava, respectively; and a fluid management plan determination unit configured to search for a fluid management plan corresponding to a score determined, and to perform volume management for the patient according to the fluid management plan searched.