General Electric Company

Ge Precision Healthcare LLC

Morpho Detection, Inc.

Ge Medical Systems Global Technology Company, LLC

Becton Dickinson and Company Limited

Prismatic Sensors Ab

Peter Michael Edic

Samit Kumar Basu

Bruno Kristiaan Bernard De Man

Yannan Jin

Jed Douglas Pack

Forrest Frank Hopkins

Geng Fu

Colin Richard Wilson

Maria Iatrou

Jiang Hsieh

Brian David Yanoff

Susanne Madeline Lee

Xue Rui

Xiaoye Wu

Robert F Senzig

Bruno De Man

Prem Venugopal

Mark Ernest Vermilyea

James Vradenburg Miller

James Walter LeBlanc

Ahmad Nadeem Ishaque

Erdogan Cesmeli

Jianjun Guo

David Allen Langan

Ricardo Scott Avila

William Robert Ross

Deborah Joy Walter

Steven James Gray

Mingye Wu

Mehmet Yavuz

Geoffrey Harding

John Eric Tkaczyk

Antonio Caiafa

Uwe Wiedmann

Biju Jacob

Zhye Yin

Soumya Ghose

Reinhold Franz Wirth

Sathish Ramani

Jhimli Mitra

Saad Ahmed Sirohey

Jean-Baptiste Thibault

Darin Robert Okerlund

Jerome F Knoplioch

John Scott Price

William Hullinger Huber

Sandeep Dutta

Paul Edgar Licato

Kai Erik Thomenius

Vance Scott Robinson

Jonathan David Short

Joseph Manak

Baojun Li

Lin Fu

Giridhar Jothiprasad

Rahul Bhotika

Cynthia Elizabeth Landberg Davis

Armin H Pfoh

John Irvin Jackson

Eberhard Neuser

Abdalmajeid Musa Alyassin

William Macomber Leue

Hewei Gao

Robert J Strowe

Bijan Dorri

Harvey E Cline

Ertugrul Berkcan

Ge Wang

Harry Kirk Mathews, Jr

Bernhard Erich Hermann Claus

Joseph Bendahan

Weizhong Yan

Yizhen Lin

Jiahua Fan

Vasile Bogdan Neculaes

Marco Francesco Aimi

Naveen Stephan Chandra

Thomas Baby Sebastian

Kai F Goebel

Robert August Kaucic

Rasiklal Punjalal Shah

Dan Xu

Xin Wang

Christopher David Unger

Melissa Liliane Vass

Douglas P Boyd

Shankara Bonthu Reddy

Scott Stephen Zelakiewicz

Sarah Kathryn Patch

Paulo Ricardo Dos Santos Mendonca

Gregory Scott Zeman

Torbjörn Hjärn

Colin Craig McCulloch

Cheng Xu

Feng Chen

Brian Joseph Graves

Denis Perrillat-Amede

Vipin Kewal Ramani

Walter Irving Garms

Wen-Tai Lin

A computer-implemented method includes obtaining, via a processor, clinical images including vessels and generating, via the processor, straightened-out images for each coronary tree path within respective clinical images, The method also includes extracting, via the processor, segmented 3D image patches, determining, via the processor, overlapping binary mask volumes for each segment, and predicting, via the processor, pressure drops across the segmented image patches using a trained deep neural network.

System and method for deep-learning based estimation of coronary artery pressure drop

A computer-implemented method includes generating, via a processor, synthetic vessels. The method also includes performing, via the processor, three-dimensional (3D) computational fluid dynamics (CFD) on the synthetic vessels for different flow rates to generate 3D CFD data. The method further includes extracting, via the processor, 3D image patches from the synthetic vessels. The method even further includes obtaining, via the processor, pressure drops across the 3D image patches from the 3D CFD data. The method yet further includes training, via the processor, a deep neural network utilizing the 3D image patches, the pressure drops, and associated flow rates to generate a trained deep neural network.

Systems and techniques that facilitate hybrid unsupervised and supervised image segmentation are provided. In various embodiments, a system can access a computed tomography (CT) image depicting an anatomical structure. In various aspects, the system can generate, via an unsupervised modeling technique, at least one class probability mask of the anatomical structure based on the CT image. In various instances, the system can generate, via a deep-learning model, an image segmentation based on the CT image and based on the at least one class probability mask.

Hybrid unsupervised and supervised image segmentation model

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.

Systems and methods for a stationary CT imaging system

A system and method for estimating vascular flow using CT imaging include a computer readable storage medium having stored thereon a computer program comprising instructions, which, when executed by a computer, cause the computer to acquire a first set of data comprising anatomical information of an imaging subject, the anatomical information comprises information of at least one vessel. The instructions further cause the computer to process the anatomical information to generate an image volume comprising the at least one vessel, generate hemodynamic information based on the image volume, and acquire a second set of data of the imaging subject. The computer is also caused to generate an image comprising the hemodynamic information in combination with a visualization based on the second set of data.

System and method for estimating vascular flow using CT imaging

The present disclosure relates to fabrication and use of a phase-contrast imaging detector that includes sub-pixel resolution electrodes or photodiodes spaced to correspond to a phase-contrast interference pattern. A system using such a detector may employ fewer gratings than are typically used in a phase-contrast imaging system, with certain functionality typically provided by a detector-side analyzer grating being performed by sub-pixel resolution structures (e.g., electrodes or photodiodes) of the detector. Measurements acquired using the detector may be used to determine offset, amplitude, and phase of a phase-contrast interference pattern without multiple acquisitions at different phase steps.

X-ray phase contrast detector

Various methods and systems are provided for multi-material decomposition for computed tomography. In one embodiment, a method comprises acquiring, via an imaging system, projection data for a plurality of x-ray spectra, estimating path lengths for a plurality of materials based on the projection data and calibration data for the imaging system, iteratively refining the estimated path lengths based on a linearized model derived from the calibration data, and reconstructing material-density images for each material of the plurality of materials from the iteratively-refined estimated path lengths. By determining path-length estimates in this way without modeling the physics of the imaging system, accurate material decomposition may be performed more quickly and with less sensitivity to changes in physics of the system, and furthermore may be extended to more than two materials.

Methods and systems for multi-material decomposition

Various methods and systems are provided for spectral computed tomography (CT) imaging. In one embodiment, a method comprises performing a scan of a subject to acquire, with a detector array comprising a plurality of detector elements, projection data of the subject, generating corrected path-length estimates based on the projection data and one or more selected correction functions, and reconstructing at least one material density image based on the corrected path-length estimates. In this way, the fidelity of spectral information is improved, thereby increasing image quality for spectral computed tomography (CT) imaging systems, especially those configured with photon-counting detectors.

Methods and systems for improved spectral fidelity for material decomposition

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

Methods and systems are provided for coherent scattered imaging using a computed tomography system with segmented detector arrays. In one embodiment, a method includes imaging a region of interest with an x-ray source and a segmented photon-counting detector array, detecting a position of an object of interest in the region of interest, selectively scanning, via the x-ray source and the segmented photon-counting detector array, the object of interest, detecting a coherent scatter signal from the object of interest with the segmented photon-counting detector array, and determining a material of the object of interest based on the detected coherent scatter signal. In this way, the coherent scatter signal may be used to identify and investigate lesions or other objects of interest within an imaged volume.

Systems and methods for coherent scatter imaging using a segmented photon-counting detector for computed tomography

There is provided an x-ray detector having a number of x-ray detector sub-modules. Each detector sub-module is an edge-on detector sub-module having an array of detector elements extending in at least two directions, wherein one of the directions has a component in the direction of incoming x-rays. The detector sub-modules are stacked one after the other and/or arranged side-by-side. For at least part of the detector sub-modules, the detector sub-modules are arranged for providing a gap between adjacent detector sub-modules, where at least part of the gap is not directed linearly towards the x-ray focal point of an x-ray source.

X-ray detector system design

The present disclosure relates to training one or more neural networks for vascular vessel assessment using synthetic image data for which ground-truth data is known. In certain implementations, the synthetic image data may be based in part, or derived from, clinical image data for which ground-truth data is not known or available. Neural networks trained in this manner may be used to perform one or more of vessel segmentation, decalcification, Hounsfield unit scoring, and/or estimation of a hemodynamic parameter.

Deep learning for arterial analysis and assessment

The present disclosure relates to determining the position of an X-ray focal spot in real time during an imaging process and using the focal spot position to ensure alignment of the focal spot and high-aspect detector elements or to correct for focal spot misalignment, thereby mitigating image artifacts. For example, the focal spot position may be monitored and may be adjusted in real-time using electromagnetic electron beam steering during a scan. Alternatively, previously determined functional relationships between focal spot position and measured data may be applied to address or correct for focal spot misalignment in the acquired data.

X-ray imaging system use and calibration

The present approaches relates to the use of silicon-based energy-discriminating, photon-counting detectors, such as for use in X-ray based imaging including computed tomography. The described approaches address the resolution and classification of X-ray photons affected by Compton scatter, which may be detected as having energy levels below their proper level due to collision or deflection events.

Energy-discriminating photon-counting detector and the use thereof

The present approach relates to the use of detector elements (i.e., reference detector pixels) positioned under septa of an anti-scatter collimator. Signals detected by the reference detector pixels may be used to correct for charging-sharing events with adjacent pixels and/or to characterize or correct for focal spot misalignment either in real time or as a calibration step.

Reference detector elements in conjunction with an anti-scatter collimator

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Albany, NY, United States of America

Peter Michael Edic