A system, device and method of imaging using a real-time, AI-enabled analysis device coupled to an imaging device during an image scan of a subject includes: receiving data corresponding to a plurality of image frames from the imaging device and user input identifying a region of interest (ROI) in a first image frame; providing data corresponding to the first image frame, including the identified ROI and data corresponding to the remaining image frames to the AI-enabled data processing system; accepting a plurality of valid image frames from the plurality of image frames based on a predefined set of computer vision rules and a minimum accepted frame threshold; calculating, frame by frame, an ROI function value of the plurality of valid image frames; determining whether a predetermined ROI function value has been reached; and alerting an operator of the imaging device that the predetermined ROI function value has been reached.

A method for controlling display of an abnormality includes obtaining a target chest X-ray image, detecting a structure including a linear structure formed of a first linear area that has been drawn by projecting anatomical structures whose X-ray transmittances are different from each other or a second linear area drawn by projecting an anatomical structure including a wall of a trachea, a wall of a bronchus, or a hair line, calculating an indicator for determining the abnormal state from the structure, comparing the indicator with a reference indicator, and determining whether the structure is in the abnormal state, and displaying, if it is determined that the structure is in the abnormal state, an image of an area of the target chest X-ray image including the structure determined to be in the abnormal state and details of the abnormal state.

Systems and methods are provided for automated identification of acute aortic syndromes in computed tomography images. A region of interest in a chest of a patient is imaged via a computed tomography (CT) scanner to provide images at a plurality of locations. The region of interest includes one of an ascending aorta, an aortic arch, and a descending aorta of the patient. For each of the plurality of locations within the region of interest, a value representing a variation in radiodensity values within the location is determined from the image to provide a set of variation values. A parameter representing a likelihood that the patient is experiencing an acute aortic syndrome is determined via a derived model from the set of variation values. The parameter representing the likelihood that the patient is experiencing the acute aortic syndrome is provided to a user at an associated output device.

An X-ray diagnostic apparatus of an embodiment includes processing circuitry. The processing circuitry determines a concentration of a contrast agent in contrast-enhanced image collection based on first reference information in which a recommended contrast in a contrast-enhanced image is associated with each region of interest to be subject to collection of the contrast-enhanced image, and on second reference information that indicates a relation among a generation condition of an X-ray, a concentration of a contrast agent, and a contrast. The processing circuitry calculates setting information of an injector to inject a contrast agent to the subject based on the determined concentration of a contrast agent.

A method of identifying a location of a region of interest within a breast utilizes compressed location coordinates for the region of interest recorded while the breast is under compression during an x-ray imaging procedure such as mammography or tomosynthesis. The compressed location coordinates are converted to uncompressed location coordinates using a mathematical tissue deformation model. The volume and density of the breast affects how the coordinates are translated for use with an ultrasound imaging system. A system including a computing system in communication with an ultrasound imaging system is utilized to perform the method. The resultant predicted location coordinates of the region of interest are used to guide a healthcare provider to potential lesions that are to be examined using ultrasound, where the potential lesions had been previously identified during a screening mammogram.

A medical image processing apparatus includes at least one processor, and the processor acquires a purpose of examination of a target medical image to be interpreted. The processor detects a first abnormal shadow from the target medical image and displays a detection result of the first abnormal shadow on a display. The processor sets, according to the purpose of the examination, a first detection threshold value for detecting the first abnormal shadow or a first display threshold value for displaying the detection result of the first abnormal shadow.

The present disclosure directs to a method for image processing. The method may include obtaining scanning data of a spine of a subject, determining one or more centrum parameters of each of a plurality of centrums of the spine based on the scanning data, and identifying at least one intervertebral disc based on the one or more centrum parameters.

Each of the at least one intervertebral disc may be between a pair of neighboring centrums of the plurality of centrums. The method may include determining an intervertebral disc reconstruction protocol of each of the at least one intervertebral disc, determining a target intervertebral disc of the at least one intervertebral disc, and reconstructing one or more images of the target intervertebral disc based on an intervertebral disc reconstruction protocol of the target intervertebral disc. The intervertebral disc reconstruction protocols may relate to MPR.

A method for training a machine learning algorithm that classifies predictive regions-of interest (“ROI”) of progression of idiopathic pulmonary fibrosis. The method includes acquiring a set of computed tomography (CT) images of a plurality of patients and selecting a plurality of ROIs within the set of images. Each of the ROIs designates a label that indicates progression of pulmonary fibrosis and training a machine learning algorithm by inputting the plurality of ROIs and the associated labels into the algorithm. The algorithm identifies the ROIs in the set of images as indicating regions of pulmonary fibrosis within the set of images based on the features.

An apparatus comprising a radiation source, coincident positron omission detectors configured to detect coincident positron annihilation emissions originating within a coordinate system, and a controller coupled to the radiation source and the coincident positron emission detectors, the controller configured to identify coincident positron annihilation emission paths intersecting one or more volumes in the coordinate system and align the radiation source along an identified coincident positron annihilation emission path.

A nuclear medicine diagnosis apparatus according to an embodiment includes a processing circuit. The processing circuit is configured to obtain nuclear medicine data; to time-divide the nuclear medicine data into at least first nuclear medicine data and second nuclear medicine data; and to identify a biological accumulation region, on the basis of a temporal change in data values included in the first nuclear medicine data and the second nuclear medicine data.