In one aspect of the invention a system and method is claimed for providing model parameters for three dimensional fabrication of anatomical structures by obtaining and reconstructing three dimensional image data with a medical imager wherein imaging acquisition parameters of the imaging system and/or reconstruction input parameters of the reconstructor are optimized for maximum geometry precision. Advantageously, the imaging system is further configured to obtain material and/or functional information of the anatomical structure model and that material information is used to incorporate the material information in the anatomical model.

A nuclear medicine (NM) multi-head imaging system is provided that includes a gantry, plural detector units mounted to the gantry, and at least one processor operably coupled to at least one of the detector units. The detector units are mounted to the gantry. Each detector unit defines a detector unit position and corresponding view oriented toward a center of the bore. Each detector unit is configured to acquire imaging information over a sweep range corresponding to the corresponding view. The at least one processor is configured to, for each detector unit, determine plural angular positions along the sweep range corresponding to boundaries of the object to be imaged, generate a representation of each angular position for each detector unit position, generate a model based on the angular positions using the representation, and determine scan parameters to be used to image the object using the model.

Systems and methods for determining an offset of a position of a focal point of an X-ray tube is provided. The methods may include obtaining at least one parameter associated with an X-ray tube during a scan of a subject. The methods may further include determining a target offset of a position of a focal point based on the at least one parameter and a target relationship between a plurality of reference parameters associated with the X-ray tube and a plurality of reference offsets of reference positions of the focal point. The methods may further include causing, based on the target offset, a correction on the position of the focal point of the X-ray tube.

Systems and methods for determining one or more target examination parameters is provided. The methods may include obtaining target examination information of a subject and generating one or more initial examination parameters based on the target examination information. The methods may further include obtaining one or more historical examination parameters associated with the subject and updating at least one of the one or more initial examination parameters based on the one or more historical examination parameters to obtain one or more target examination parameters. The one or more target examination parameters may be used for performing a target examination on the subject.

A method for controlling a scanner comprises: sensing an outer surface of a body of a subject to collect body surface data, using machine learning to predict a surface of an internal organ of the subject based on the body surface data, and controlling the scanner based on the predicted surface of the internal organ.

Techniques are described for tailoring automatic exposure control (AEC) settings to specific patient anatomies and clinical tasks. According to an embodiment, computer-implemented method comprises receiving one or more scout images captured of an anatomical region of a patient in association with performance of a computed tomography (CT) scan. The method further comprises employing a first machine learning model to estimate, based on the one or more scout images, expected organ doses representative of expected radiation doses exposed to organs in the anatomical region under different AEC patterns for the CT scan. The method can further comprises employing a second machine learning model to estimate, based on the one or more scout images, expected measures of image quality in target and background regions of scan images captured under the different AEC patterns, and determining an optimal AEC pattern based on the expected organ doses and the expected measures of image quality.

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 and system for determining a PET image of the scan volume based on one or more PET sub-images is provided. The method may include determining a scan volume of a subject supported by a scan table; dividing the scan volume into one or more scan regions; for each scan region of the one or more scan regions, determining whether there is a physiological motion in the scan region; generating, based on a result of the determination, a PET sub-image of the scan region based on first PET data of the scan region acquired in a first mode or based, at least in part, on second PET data of the scan region acquired in a second mode; and generating a PET image of the scan volume based on one or more PET sub-images.

Provided is a medical imaging apparatus having an AR-visualization module operably coupled to a camera and to a position determination module, which is adapted to create an AR-image based on an image received from the camera and an AR-overlay positionally registered with the image, and which includes a display interface adapted to transmit the created AR-image to a medical display.

Healthcare services can be automated utilizing a system that recognizes at least one characteristic of a patient based on images of the patient acquired by an image capturing device. Relying on information extracted from these images, the system may automate multiple aspects of a medical procedure such as patient identification and verification, positioning, diagnosis and/or treatment planning using artificial intelligence or machine learning techniques. By automating these operations, healthcare services can be provided remotely and/or with minimum physical contact between the patient and a medical professional.