General Electric Company

Ge Precision Healthcare LLC

University Of Zürich

Amy Deubig

Laurent Launay

Sandeep Dutta

Sidharth Abrol

John Howard Londt

Bipul Das

John Irvin Jackson

Scott McOlash

Christine Carol Hammond

Vanika Singhal

Daphné Gerbaud

Bianca Sintini

Ronny Büchel

Ronan Quelever

Philipp Kaufmann

Vignesha Ramegowda

Systems/techniques that facilitate learning-based domain transformation for medical images are provided. In various embodiments, a system can access a medical image. In various aspects, the medical image can depict an anatomical structure according to a first medical scanning domain. In various instances, the system can generate, via execution of a machine learning model, a predicted image based on the medical image. In various aspects, the predicted image can depict the anatomical structure according to a second medical scanning domain that is different from the first medical scanning domain. In some cases, the first and second medical scanning domains can be first and second energy levels of a computed tomography (CT) scanning modality. In other cases, the first and second medical scanning domains can be first and second contrast phases of the CT scanning modality.

Learning-based domain transformation for medical images

An imaging system and method acquires non-contrast images of a region of interest in a body and determines an entrance criterion based on the non-contrast images. The entrance criterion dictates conditions in which to begin acquiring contrast imaging exposures of the region of interest. An amount of a contrast agent is measured in one or more locations in the imaged body subsequent to acquiring the non-contrast images. The system and method determine that the one or more conditions of the entrance criterion are met based on the amount of the contrast agent that is measured in the imaged body, and acquire one or more contrast images of the region of interest in the imaged body responsive to determining that the one or more conditions of the entrance criterion are met.

Contrast imaging system and method

A method of displaying image data for a tissue of an organ includes acquiring a three-dimensional (3D) projection dataset using a Computed Tomography (CT) imaging system, performing a segmentation of the 3D projection dataset that includes a plurality of voxels, performing a perfusion viability cluster analysis to identify myocardium voxels, grouping the myocardium voxels into viable clusters and non-viable clusters based on a density and a location of the myocardium voxels, and generating an image of the myocardium and a coronary tree using the viable clusters and the non-viable clusters. An imaging system and a non-transitory computer readable medium are also described herein.

Method and apparatus for generating a perfusion image

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Dousman, WI, United States of America

Amy Deubig