General Electric Company

Ge Precision Healthcare LLC

Purdue Research Foundation

University of Notre Dame Du Lac

University of Michigan

Rensselaer Polytechnic Institute

Bruno Kristiaan Bernard De Man

Peter Michael Edic

Samit Kumar Basu

Lin Fu

Jiang Hsieh

Jed Douglas Pack

Jean-Baptiste Thibault

Zhou Yu

Kai Zeng

Sathish Ramani

Charles Addison Bouman

Zhye Yin

Yannan Jin

Ken David Sauer

Jeffrey Allen Fessler

Jiahua Fan

Xue Rui

Debashish Pal

Mingye Wu

Uwe Wiedmann

Naveen Stephan Chandra

Xiaoye Wu

Robert F Senzig

James Vradenburg Miller

William Robert Ross

Jean-Baptiste Daniel Marie Thibault

Eri Haneda

Ge Wang

John Eric Tkaczyk

Mark Ernest Vermilyea

Bernhard Erich Hermann Claus

Brian David Yanoff

Vasile Bogdan Neculaes

Biju Jacob

Chad Allan Smith

Ross Christopher Stalter

Baojun Li

Gautam Parthasarathy

Sangtae Ahn

Maria Iatrou

Thomas Matthew Benson

Roy-Arnulf Helge Nilsen

Somesh Srivastava

Xiaoyu Tian

John Lawrence Seamans

Kyle Morgan Champley

Donghwan Kim

Jong Chul Ye

Douglas Albagli

Antonio Caiafa

Sergio Lemaitre

Daniel David Harrison

Brian Edward Nett

Thomas Kwok-Fah Foo

William Andrew Hennessy

Jianjun Guo

Abdelaziz Ikhlef

David Allen Langan

Colin Richard Wilson

James Walter LeBlanc

Nicholas Ryan Konkle

Mark Alan Frontera

Ali Can

Jeffrey Alan Kautzer

Jonathan David Short

Souma Sengupta

Thomas Baby Sebastian

Ricardo Scott Avila

Geng Fu

Paavana Sainath

Deborah Joy Walter

Jang Hwan Cho

Christopher David Unger

Michael John Utschig

Prem Venugopal

Scott Stephen Zelakiewicz

Paulo Ricardo Dos Santos Mendonca

William Macomber Leue

Paulo Ricardo Mendonca

Colin Craig McCulloch

Feng Chen

Kedar Bhalchandra Khare

Guangzhi Cao

Denis Perrillat-Amede

Katsumasa Nose

Evgeny Drapkin

Jie Tang

Mark Adamak

Yan Xi

Peter Claudius Sanza

Jean-Francois Larroux

Jeffrey Seymour Gordon

Adam Budde

Qi Zhao

Roy Arnulf Helge Nilsen

Ronald A Von Jako

Eric Patrick Davis

Brian Patrick Smyth

Paul Francis Fitzgerald

Zhanfeng Xing

Various embodiments discussed herein utilize a C-shaped imager to provide images with a minimal footprint, such as may be suitable in a surgical context. In addition the systems and methods described herein allow for suitable angular (i.e., azimuthal) scan coverage about the patient. To provide real-time 3D imaging, multiple X-ray tubes or a distributed X-ray source may be employed, coupled with an extended detector or multiple detectors. To reconstruct high-quality volumes, in some implementations reconstruction techniques may be employed that utilize pre-operative (pre-op) computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (U/S), or other suitable modality images or data as prior information.

Intra-surgery imaging system

There is provided an X-ray imaging system having an X-ray source, an X-ray detector and a collimator assembly in the X-ray path between the X-ray source and the X-ray detector. The X-ray detector comprises a plurality of detector modules arranged side-by-side and adapted to be oriented towards the X-ray source, the detector modules being arranged side-by-side along a direction substantially orthogonal to the direction of incoming X-rays. The collimator assembly is based on a plurality of spaced collimator plates arranged side-by-side in a direction coinciding with the direction of the detector modules. The collimator assembly further comprises a physically stabilizing lateral support structure arranged in a lateral plane extending in a direction substantially orthogonal to the direction of incoming X-rays.

Collimator assembly for an X-ray detector

A method for forming a multi-layered, stacked grid structure includes aligning a first grid structure with a second grid structure, wherein both the first grid structure and the second grid structure each include a substrate in which a plurality of trenches are formed and a cured carrier fluid disposed within the plurality of trenches, and wherein a plurality of nano-particles are suspended within the cured carrier fluid. The method also includes, upon aligning the first grid structure and the second grid structure so that their respective plurality of trenches are aligned in the same orientation, joining the first grid structure and the second grid structure together to form the multi-layered, stacked grid structure.

Aligned and stacked high-aspect ratio metallized structures

Various methods and systems are provided for stationary CT imaging. In one embodiment, a modular imaging system comprises a plurality of distributed x-ray units releasably coupled to a plurality of detector arrays, with the plurality of distributed x-ray units and the plurality of detector arrays forming a self-supporting structure including a central opening shaped to receive a subject to be imaged. The modular imaging system may image the subject without rotation of the distributed x-ray units or detector arrays around the subject.

Systems and methods for a stationary CT imaging system

Various methods and systems are provided for stationary computed tomography (CT) imaging. In one embodiment, a stationary CT system includes one or more detector arrays extending around at least a portion of an imaging volume, a stationary distributed x-ray source unit comprising a plurality of emitters including a first set of emitters configured to operate at a first voltage and a second set of emitters configured to operate at a second voltage, different than the first voltage, and a source controller for triggering the first set of emitters for acquiring first projection data by the one or more detector arrays and triggering the second set of emitters for acquiring second projection data by the one or more detector arrays, the first projection data and the second projection data usable to reconstruct one or more basis material composition images or monochromatic images of an object within the imaging volume.

Various methods and systems are provided for stationary CT imaging. In one embodiment, an imaging system comprises a stationary distributed x-ray source unit comprising a plurality of emitters positioned to emit x-ray beams through the imaging volume, one or more detector arrays extending around at least a portion of an imaging volume, each detector array comprising a plurality of detector elements, each detector element configured to receive x-ray beams from more than one emitter, and an anti-scatter device configured to be positioned between one or more emitters of the plurality of emitters and an object in the imaging volume.

Various methods and systems are provided for stationary CT imaging. In one embodiment, an imaging system comprises a chamber shaped to enclose a subject to be imaged, a support surface disposed within the chamber and shaped to maintain the subject in an upright position, and an annular imaging unit encircling the chamber and having a fixed angular orientation to the chamber, the annular imaging unit including a distributed x-ray unit and a detector array arranged opposite to each other across the chamber. The imaging system may image the subject without rotation of the annular imaging unit.

A computer-implemented method for image processing is provided. The method includes obtaining data acquired by a medical imaging system. The method also includes normalizing the data. The method further includes de-noising the normalized data utilizing a deep learning-based denoising network. The method even further includes de-normalizing the de-noised data. The method yet further includes generating blended data based on both the data and the de-normalized de-noised data.

System and method for normalizing dynamic range of data acquired utilizing medical imaging

Various methods and systems are provided for stationary CT imaging. In one embodiment, a method for an imaging system includes activating a plurality of emitters of a stationary distributed x-ray source unit to emit x-ray beams toward an object within an imaging volume, where the x-ray source unit does not rotate around the imaging volume, receiving attenuated x-ray beams with one or more detector arrays to form a sparse view projection dataset, where each attenuated x-ray beam generates a different view, and reconstructing an image from the sparse view projection dataset using a sparse view reconstruction method.

Various methods and systems are provided for stationary CT imaging. In one embodiment, a method for an imaging system includes activating an emitter of a plurality of emitters of a stationary distributed x-ray source unit to emit an x-ray beam toward an object within an imaging volume, where the x-ray source unit does not rotate around the imaging volume, receiving the x-ray beam at a subset of detector elements of a plurality of detector elements of one or more detector arrays, sampling the plurality of detector elements to generate a total transmission profile, an attenuation profile, and a scatter measurement, generating a scatter-corrected attenuation profile by entering the total transmission profile, the attenuation profile, and the scatter measurement as inputs to a model, and reconstructing one or more images from the scatter-corrected attenuation profile.

The present disclosure relates to the use of X-ray detector cassettes that may be abutted or overlapped to form a detector assembly suitable for imaging objects that are too large to image using a single X-ray detector cassette. Such a detector assembly may be customized in terms of the size and/or shape of the field-of-view (FOV). In certain embodiments the radiation-sensitive electronics (e.g., readout electronics) are positioned to the side of the X-ray detecting components (e.g., scintillator, TFT array, and so forth), allowing the cassette to be thin relative to other detector devices and allowing the electronics to remain outside the X-ray beam path.

Tileable X-ray detector cassettes

The present disclosure relates to image reconstruction with favorable properties in terms of noise reduction, spatial resolution, detail preservation and computational complexity. The disclosed techniques may include some or all of: a first-pass reconstruction, a simplified datafit term, and/or a deep learning denoiser. In various implementations, the disclosed technique is portable to different CT platforms, such as by incorporating a first-pass reconstruction step.

Iterative image reconstruction framework

A computer-implemented method for image processing is provided. The method includes acquiring multiple multi-energy spectral scan datasets and computing basis material images representative of multiple basis materials from the multi-energy spectral scan datasets, wherein the multiple basis material images include correlated noise. The method also includes jointly denoising the multiple basis material images in at least a spectral domain utilizing a deep learning-based denoising network to generate multiple de-noised basis material images.

System and method for processing data acquired utilizing multi-energy computed tomography imaging

A signal processing method is disclosed, which includes detecting a total intensity of X-rays passing through an object comprising multiple materials; obtaining at least one set of basis information of basis material information of the multiple materials and basis component information of photon-electric absorption basis component and Compton scattering basis component of the object; estimating a scatter intensity component of the detected X-rays based on the at least one set of basis information and the detected total intensity; and obtaining an intensity estimate of primary X-rays incident on a detector based on the detected total intensity and the estimated scatter intensity component. An imaging system adopting the above signal processing method is also disclosed.

Signal processing method and imaging system for scatter correction in computed tomography

Growing community of inventors

Clifton Park, NY, United States of America

Bruno Kristiaan Bernard De Man