The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, perform computational fluid dynamics analysis, facilitate assessment of risk of heart disease and coronary artery disease, enhance drug development, determine a CAD risk factor goal, provide atherosclerosis and vascular morphology characterization, and determine indication of myocardial risk, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

Embodiments of the present disclosure are related to providing a method and device processing a first set of volumetric image data comprising cross-sectional images of a myocardium and displaying a second set of volumetric image data of the myocardium. A curved plane to rectangular plane transformation of cross-sectional images of myocardium of human heart is proposed. After the transformation, a combined and reconstructed set of myocardium images are superimposed with a modified Bull's Eye View (BEV) map and corresponding parameters indicating extent of fibrosis to obtain a second set of volumetric image data of myocardium. In addition to quantifying and displaying the extent of fibrosis, the proposed solution preserves neighborhood and adjacency criteria of abnormal tissues of myocardium walls of human heart.

A method for optimizing an ultrasonic imaging system parameter based on deep learning, comprising the following steps: step 1: collecting samples for training neural networks, the samples comprising ultrasound image samples i, and a corresponding ultrasonic imaging system parameter vector sample p used by an ultrasonic imaging system when the ultrasonic image samples are collected; step 2: establishing a neural network model and training the neural networks to convergence by using the samples collected=in step 1, so as to obtain a trained neural network system onn; and step 3: taking the original ultrasonic imaging system parameter vector p or the original ultrasonic image as an input to be input into the neural network system onn trained in step 2, at this moment, a parameter obtained from an output end of onn being an optimized ultrasonic imaging system parameter vector ep=onn(p). By means of the method, the purpose of improving the ultrasonic image quality is realized by optimizing the ultrasonic imaging system parameter.

A medical image processing apparatus according to an embodiment includes processing circuitry configured to generate an output data set apparently expressing a second data set obtained by transmitting and receiving an ultrasound wave, for each scanning line, as many times as a second number that is larger than a first number, by inputting a first data set to a trained model that generates the output data set on a basis of the first data set obtained by transmitting and receiving an ultrasound wave as many times as the first number for each scanning line.

An analyzing apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to calculate a tissue characteristic parameter value with respect to each of a plurality of positions within a region of interest, by analyzing a result of a scan performed on a patient. The processing circuitry is configured to determine a measurement region in the region of interest by performing an analysis while using the tissue characteristic parameter values. The processing circuitry is configured to calculate a statistic value of the tissue characteristic parameter values in the measurement region.

Systems and methods for improving soft tissue contrast, characterizing tissue, classifying phenotype, stratifying risk, and performing multi-scale modeling aided by multiple energy or contrast excitation and evaluation are provided. The systems and methods can include single and multi-phase acquisitions and broad and local spectrum imaging to assess atherosclerotic plaque tissues in the vessel wall and perivascular space.

Methods of processing images, such as ultrasound images, to determine integrity of an implant are described. The method may include receiving an ultrasound image of an implant in a body of a subject; determining one or more characteristics of a surface of the implant based on an intensity of pixels of the ultrasound image; generating a predicted status of the implant based on the one or more characteristics by comparison of the one or more characteristics with a database of image data; and displaying the predicted status of the implant. The implant may be a breast implant, for example, wherein the method is useful for analyzing the presence or probability of extracapsular ruptures, contractures, and combinations thereof.

Systems and methods are provided for enhanced heart medical imaging operations, particularly as by incorporating use of artificial intelligence (AI) based fetal heart functional analysis and/or real-time and automatic ejection fraction (EF) measurement and analysis.

Methods and systems are provided for dynamically selecting ultrasound transmits. In one example, a method includes dynamically updating a number of transmit lines and/or a pattern of transmit lines for acquiring an ultrasound image based on a prior ultrasound image and a task to be performed with the ultrasound image, and acquiring the ultrasound image with an ultrasound probe controlled to operate with the updated number of transmit lines and/or the updated pattern of transmit lines.

The invention relates to a system (1) for reviewing 3D or 4D medical image data (2), the system (1) comprising (a) a medical review application (MRA) (4) comprising a processing module (6) configured to process a 3D or 4D dataset (2) to generate 3D content (8), and a 2D user interface (16); wherein the 2D user interface (16) is configured to display the 3D content (8) and to allow a user (30) to generate user input (18) commands; (b) an extended reality (XR)-based user interface add-on (XRA) (100); and (c) a data exchange channel (10), the data exchange channel (10) being configured to interface the processing module (6) with the XRA (100); wherein the XRA (100) is configured to interpret and process the 3D content (8) and convert it to XR content displayable to the user (30) in an XR environment (48); wherein the XR environment (48) is configured to allow a user to generate user input (18) events, and the XRA (100) is configured to process the user input (18) events and convert them to user input (18) commands readable by the MRA (4). The invention also relates to an extended reality-based user interface add-on (100), a related method for analysing a 3D or 4D dataset (2), and a related computer program.