General Electric Company

Ge Precision Healthcare LLC

University of Notre Dame Du Lac

Jiahua Fan

Jiang Hsieh

Bruno Kristiaan Bernard De Man

Naveen Stephan Chandra

Guangzhi Cao

Jed Douglas Pack

Dominic Joseph Crotty

Uwe Wiedmann

Yannan Jin

Priti Madhav

Jean-Baptiste Thibault

Zhye Yin

Masatake Nukui

John Moore Boudry

Fang Frank Dong

Hewei Gao

Mingye Wu

Grant Morey Stevens

Changlyong Kim

Franco Rupcich

Roy-Arnulf Helge Nilsen

Mary Sue Kulpins

Meghan Lynn Yue

Xiao Jin

Roman Melnyk

Suresh Narayanan Narayanan

Yujiro Yazaki

Peter Michael Edic

Thomas L Toth

Jonathan S Maltz

Antonio Caiafa

Sergio Lemaitre

Brian Edward Nett

Scott David Wollenweber

Nicholas Ryan Konkle

Mark Alan Frontera

Ken David Sauer

Dan Xu

Xiangyang Tang

Michael John Utschig

Kai Zeng

Xue Rui

Roy A Nilsen

Sathish Ramani

Adam Israel Cohen

Feng Chen

Thomas Matthew Benson

Adam Gregory Pautsch

David Joseph Pitterle

Ming Yan

Zhoubo Li

Jean-Francois Larroux

Jie Tang

Adam Budde

Ryan James Lemminger

Roy Arnulf Helge Nilsen

Xiaoyu Tian

Peter S Crandall

Eric Biehr

Tal Gilat Schmidt

Andrew C Johnson

Yirong Yang

Scott D Slavic

Vijay Subramanian

Joshua Thomas Lamb

Amber Christine Silvaggio

Douglas Grant

Johanna Sumner

Kentaro Ogata

Ken Arai

Norbert Joseph Pelc

Brian J Breuer

Håkan Langemark

Brody DeSilva

Carrigan Hudgins

Matthew Getzin

Rajeshwari Karthikeyan

Parag Khobragade

Tonghe Wang

Edgar Chino

Yunzhe Zhao

Methods and systems are provided for reconstructing images within a photon-counting computed tomography (PCCT) system. In an example, a method comprises, during a scan of an imaging subject, obtaining photon counts from a detector element of a photon-counting detector of the PCCT system, the photon counts partitioned into a plurality of energy bins based on an energy imparted by each photon on the detector element; encoding the photon counts at the plurality of energy bins of the detector element into a single scalar output value, the single scalar output value representing a distribution of spectral information across the energy bins; and reconstructing an image from projection data acquired via the photon-counting detector, the projection data including the single scalar output value generated at the detector element; wherein a basis material decomposition process is not performed during image reconstruction.

Systems and methods for CT image reconstruction

Systems and methods are provided for increasing a quality of computed tomography (CT) images. In one embodiment, a computed tomography (CT) detector system comprises a layer of energy integrating detectors (EID) arranged below a layer of photon counting (PC) sensors with respect to an incoming x-ray, where a number of the PC sensors exceeds a number of the EID detectors; and an image processing unit configured to correct PC data using EID data, and denoise and increase a resolution of an image reconstructed from EID data and PC data using a deep learning convolutional neural network (CNN) trained on pairs of images, each pair of images including a target image reconstructed from a first signal from the layer of PC sensors, and an input image reconstructed from a second signal from the layer of EID detectors, the EID data and PC data acquired concurrently from a same patient ray path.

Systems and methods for computed tomography

There is provided a computed tomography imaging system including a gantry including a rotating member on a rotating side, a stationary member on a stationary side, and a data communication system. The rotating member on the rotating side includes an X-ray source configured to emit X-rays, an X-ray detector configured to generate detector data, a data storage unit configured to store the detector data, and processing circuitry configured to process at least part of the stored detector data to generate a processed data set. The stationary member on the stationary side is communicatively coupled to the rotating member on the rotating side, and the data communication system is configured to transfer the processed data set from the rotating member on the rotating side to the stationary member on the stationary side.

Adaptive data acquisition for computed tomography systems

Methods and systems are provided for increasing a quality of computed tomography (CT) images. In one embodiment, a method for a photon-counting computed tomography (PCCT) system comprises, adjusting an X-ray tube output current of the PCCT system across and/or within one or more views, the current adjusted between a first current and a second current, the first current higher than the second current; for a view of the one or more views scanned by the PCCT system, applying a first pile-up correction to a first photon count output at each detector of a detector array of the PCCT system at the first current, the first pile-up correction calculated based on a second pile-up correction applied to a second photon count output at each detector at the second current; and reconstructing an image based on the corrected first photon count and the corrected second photon count.

Methods and systems are provided for contrast-to-noise evaluation in medical imaging systems. In one embodiment, a method includes positioning a phantom having a variable width cross-section within a gantry of a computed tomography (CT) system so that the variable width cross-section is perpendicular to a central axis of the CT system, adjusting the phantom within the gantry of the CT system to a first imaging configuration having a first position and a first translation within the gantry, acquiring a first set of measurements from the phantom in the first imaging configuration, and calculating a contrast-to-noise ratio (CNR) of the CT system based on at least the first set of measurements and a first material density of an imaged slice of the phantom in the first imaging configuration.

Methods and systems for contrast-to-noise evaluation of computed tomography systems

Computer processing techniques are described for reducing streaks in computed tomography (CT) images. According to an embodiment, computer-implemented method comprises obtaining, by a system comprising a processor, a pair of CT images reconstructed from a same set of projection data, the pair comprising a first image reconstructed from the projection data using a standard reconstruction process and a second image reconstructed from the projection data using a filtering reconstruction process that results in the second image comprising a first reduced level of streaks relative to the first image. The method further comprises generating, by the system, a third image by fusing a first subset of pixels extracted from one or more non-uniform areas in the first image and a second subset of pixels extracted from one or more uniform areas in the second image, wherein the third image comprises a second reduced level of streaks relative to the first image.

Computer processing techniques for streak reduction in computed tomography images

A method includes acquiring a first dataset of projection measurements at a first energy spectrum and a second dataset of projection measurements at a second energy spectrum different from the first energy spectrum by switching between acquiring the first dataset for a set number of consecutive views at different projection angles at the first energy spectrum and acquiring the second dataset for the set number of consecutive views at different projection angles at the second energy spectrum. The set number of consecutive views is greater than one. The method includes supplementing both the first dataset with estimated projection measurements at the first energy spectrum and the second dataset with estimated projection measurements at the second energy spectrum to provide missing projection measurements at different projection angles not acquired during the imaging scan for the first dataset and the second dataset.

System and method for utilizing dual energy imaging in a computed tomography imaging system

Methods and systems are provided for improving image quality with three-dimensional (3D) scout scans for computed tomography (CT) imaging. In one embodiment, a method comprises reconstructing an image from projection data acquired during a diagnostic scan of a patient with corrections based on scout projection data acquired during a 3D scout scan of the patient. In this way, the image quality of a diagnostic image can be improved by using 3D scout data to correct projection data or image data.

Systems and methods for improving image quality with three-dimensional scout

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

Methods and systems are provided for reconstructing images with a tailored image texture. In one embodiment, a method comprises acquiring projection data, and reconstructing an image from the projection data with a desired image texture. In this way, iterative image reconstruction techniques may be used to substantially reduce image noise, thereby enabling a reduction in injected contrast and/or radiation dose, while preserving an image texture familiar from analytic image reconstruction techniques.

Systems and methods for tailored image texture in iterative image reconstruction

Methods and systems are provided for dual energy imaging. In one embodiment, a method for a dual energy imaging system comprises determining a first tube potential and a second tube potential according to a size of a subject, and controlling the dual energy imaging system with the first tube potential and the second tube potential to generate lower energy x-rays and higher energy x-rays respectively to image the subject. In this way, image quality may be increased while minimizing dose during dual energy imaging of a particular imaging subject.

Systems and methods for automatic tube potential selection in dual energy imaging

Systems, apparatus, methods, and computer-readable storage media to estimate scatter in an image are disclosed. An example apparatus includes a network generator to generate and train an inception neural network using first and second input to deploy an inception neural network model to process image data when first and second outputs of the inception neural network converge, the first input based on a raw sinogram of a first image and the second input based on an attenuation-corrected sinogram of the first image, the inception neural network including a first filter of a first size and a second filter of a second size in a layer to process the first input and/or the second input to generate an estimate of scatter in the first image. The example apparatus also includes an image processor to apply the estimate of scatter to a second image to generate a processed image.

Deep learning-based PET scatter estimation systems and methods using an inception neural network model

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 computed tomography (CT) imaging system and method, wherein the system includes an x-ray source that is operable to emit a beam of x-rays from a focal spot and move a spot position of the focal spot. The system also includes a detector assembly that is configured to detect the x-rays attenuated by the object. At least one processing unit is configured to execute programmed instructions stored in memory. The at least one processing unit is configured to direct the x-ray source to emit different beams of the x-rays at different energy levels and to receive data from the detector assembly that are representative of detection of the x-rays emitted at the different energy levels. The at least one processing unit is also configured to direct the x-ray source to move the focal spot such that the focal spot is at different spot positions while the different beams are emitted.

Computed tomography system and method configured to image at different energy levels and focal spot positions

A method is provided that includes acquiring initial PET imaging data. The method also includes acquiring CT imaging data. Further, the method includes training a deep learning model for PET image reconstruction using the initial PET imaging data and the CT imaging data.

Systems and methods for improved PET imaging

Growing community of inventors

New Berlin, WI, United States of America

Jiahua Fan