General Electric Company

Ge Precision Healthcare LLC

University of Michigan

Purdue Research Foundation

University of Notre Dame Du Lac

Evgeny Drapkin

Jean-Baptiste Thibault

David Ruimi

Jerome Stephen Arenson

Debashish Pal

Roy A Nilsen

Mary Sue Kulpins

Jiang Hsieh

Samit Kumar Basu

Bruno Kristiaan Bernard De Man

John Eric Tkaczyk

Charles Addison Bouman

Xiaoye Wu

Brian Edward Nett

Yanfeng Du

Ken David Sauer

Lev Greenberg

Zhou Yu

Lin Fu

Jeffrey Allen Fessler

Kai Zeng

Oded Meirav

Guangzhi Cao

Bruno K B De Man

Somesh Srivastava

David Erik Chevalier

Donghwan Kim

Fausto Espinal

Madison Gray McGaffin

Michael Braunstein

David Minor

Ben Dayan

Greg Ohme

Manoj Unnikrishnan

Ryan Thome

Dmitry Samsonov

Techniques regarding the management of computational resources based on clinical priority associated with one or more computing tasks are provided. For example, one or more embodiments described herein can regard a system comprising a memory that can store computer-executable components. The system can also comprise a processor, operably coupled to the memory, that executes the computer-executable components stored in the memory. The computer-executable components can include a prioritization component that can prioritize computer applications based on a clinical priority of tasks performed by the computer applications. The clinical priority can characterize a time sensitivity of the tasks. The computer-executable components can also include a resource pool component that can divide computational resources across a plurality of resource pools and can assign the computer applications to the plurality of resource pools based on the clinical priority.

Managing computer resources for clinical applications

Methods and systems are provided for high-resolution computed tomography imaging. In one embodiment, a method comprises detecting, with a detector array comprising a plurality of individual detectors each configured with a first number of energy bins, photons generated by an x-ray source and attenuated by a subject to be imaged, generating a first dataset, for a virtual detector array comprising a combination of individual detectors and macro-detectors, by selectively aggregating a subset of the individual detectors into the macro-detectors with a second number of energy bins, generating a second dataset, for an augmented detector array comprising the plurality of individual detectors each configured with the second number of energy bins, by up-sampling the first dataset, and reconstructing an image of the subject from the second dataset. In this way, high-resolution detector arrays can acquire minimal data while maintaining a high image resolution and spectral resolution.

Systems and methods for high-resolution spectral computed tomography imaging

A method for iteratively reconstructing an image is provided. The method includes acquiring, with a detector, computed tomography (CT) imaging information. The method also includes generating, with at least one processor, sinogram information from the CT imaging information. Further, the method includes generating, with the at least one processor, image domain information from the CT imaging information. Also, the method includes updating the image using the sinogram information. The method further includes updating the image using the image domain information. Updating the image using the sinogram information and updating the image using the image domain information are performed separately and alternately in an iterative fashion.

Systems and methods for parallel processing of imaging information

A method for reconstructing an image of an object that includes a plurality of image elements. The method includes accessing image data associated with a plurality of image elements, and reconstructing an image of the object by optimizing an objective function, where the objective function is optimized by iteratively solving a nested sequence of approximate optimization problems. The algorithm is composed of nested iterative loops, in which an inner loop iteratively optimizes an objective function approximating the outer loop objective function, and an outer loop that utilizes the solution of the inner loop to optimize the original objective function.

Method and apparatus for iterative reconstruction

A method and apparatus for processing raw image data to create processed images. Raw image data is acquired. The raw image data is decomposed by a data decomposer into N subsets of raw image data. The number N is based on a number of available image generation processors. The N subsets of raw image data are processed by at least one image generation processor to create processed image data. If more than one image generation processor is available, the image generation processors perform image processing on the raw image data in parallel with respect to each other.

Method and apparatus for image reconstruction using data decomposition for all or portions of the processing flow

An adaptive CT data acquisition system and technique is presented whereby radiation emitted for CT data acquisition is dynamically controlled to limit exposure to those detectors of a CT detector assembly that may be particularly susceptible to saturation during a given data acquisition. The data acquisition technique recognizes that for a given subject size and position that pre-subject filtering and collimating of a radiation beam may be insufficient to completely prevent detector saturation. Therefore, the present invention includes implementation of a number of CT data correction techniques for correcting otherwise unusable data of a saturated CT detector. These data correction techniques include a nearest neighbor correction, off-centered phantom correction, off-centered synthetic data correction, scout data correction, planar radiogram correction, and a number of others. The invention is applicable with energy discriminating CT systems as well as with conventional CT systems and other multi-energy CT systems, such as dual kVp-based systems.

Method and system of CT data correction

Growing community of inventors

Delafield, WI, United States of America

Evgeny Drapkin