General Electric Company

Marquette University

Tal Gilat Schmidt

Kevin C Zimmerman

Darin Robert Okerlund

Brian Edward Nett

Jiahua Fan

Sandeep Dutta

Dominic Joseph Crotty

Franco Rupcich

David Pazzani

Daniel Stassi

Parag Khobragade

Computed tomography (CT) imaging system has at least one processing unit configured to receive operator inputs that include a modified system feature and a clinical task having a task object and also receive operator inputs for determining a task-based image quality (IQ) metric. The task-based IQ metric represents a desired overall image quality of image data for performing the clinical task. The image data acquired using a reference system feature. The at least one processing unit is also configured to determine an exposure-control parameter based on the task object, the modified system feature, and the task-based IQ metric. The at least one processing unit is also configured to direct the x-ray source to generate the x-ray beam during the CT scan, wherein at least one of the tube current or the tube potential during the CT scan is a function of the exposure-control parameter.

CT imaging system and method using a task-based image quality metric to achieve a desired image quality

A method of processing x-ray images comprises training an artificial neural network to process multi-spectral x-ray projections to determine composition information about an object in terms of equivalent thickness of at least one basis material. The method further comprises providing a multi-spectral x-ray projection of an object, wherein the multi-spectral x-ray projection of the object contains energy content information describing the energy content of the multi-spectral x-ray projection, The multi-spectral x-ray projection is then processed with the artificial neural network to determine composition information about the object, and then the composition information about the object is provided

Material decomposition of multi-spectral X-ray projections using neural networks

A method of processing x-ray images comprises training an artificial neural network to process multi-spectral x-ray projections to determine composition information about an object in terms of equivalent thickness of at least one basis material. The method further comprises providing a multi-spectral x-ray projection of an object, wherein the multi-spectral x-ray projection of the object contains energy content information describing the energy content of the multi-spectral x-ray projection. The multi-spectral x-ray projection is then processed with the artificial neural network to determine composition information about the object, and then the composition information about the object is provided.

Material decomposition of multi-spectral x-ray projections using neural networks

A method is provided including determining at least one range of phases of a cardiac cycle from which to select a selected phase based on at least one of patient demographic information, patient physiological information, or a general physiological model. The method also includes generating corresponding intermediate images for each of the phases of the at least one range of phases. Further, the method includes selecting the selected phase based on at least one image quality (IQ) metric of the intermediate images. Also, the method includes generating an image for diagnostic use using imaging information from the selected phase.

Systems and methods for coronary imaging

Growing community of inventors

Whitefish Bay, WI, United States of America

Tal Gilat Schmidt