DEEP NEURAL NETWORK FOR CT METAL ARTIFACT REDUCTION

    公开(公告)号:US20210000438A1

    公开(公告)日:2021-01-07

    申请号:US16978258

    申请日:2019-03-06

    摘要: A deep neural network for metal artifact reduction is described. A method for computed tomography (CT) metal artifact reduction (MAR) includes generating, by a projection completion circuitry, an intermediate CT image data based, at least in part, on input CT projection data. The intermediate CT image data is configured to include relatively fewer artifacts than an uncorrected CT image reconstructed from the input CT projection data. The method further includes generating, by an artificial neural network (ANN), CT output image data based, at least in part, on the intermediate CT image data. The CT output image data is configured to include relatively fewer artifacts compared to the intermediate CT image data. The method may further include generating, by detail image circuitry, detail CT image data based, at least in part, on input CT image data. The CT output image data is generated based, at least in part, on the detail CT image data.

    STATIONARY IN-VIVO GRATING-ENABLED MICRO-CT ARCHITECTURE (SIGMA)

    公开(公告)号:US20200261030A1

    公开(公告)日:2020-08-20

    申请号:US16761543

    申请日:2018-11-06

    IPC分类号: A61B6/03 A61B6/00

    摘要: A stationary in-vivo grating-enabled micro-CT (computed tomography) architecture (SIGMA) system includes CT scanner control circuitry and a number of imaging chains. Each imaging chain includes an x-ray source array, a phase grating, an analyzer grating and a detector array. Each imaging chain is stationary and each x-ray source array includes a plurality of x-ray source elements. Each imaging chain has a centerline, the centerlines of the number of imaging chains intersect at a center point and a first angle between the centerlines of a first adjacent pair of imaging chains equals a second angle between the centerlines of a second adjacent pair of imaging chains. A plurality of selected x-ray source elements of a first x-ray source array is configured to emit a plurality of x-ray beams in a multiplexing fashion.

    Deep neural network for CT metal artifact reduction

    公开(公告)号:US11589834B2

    公开(公告)日:2023-02-28

    申请号:US16978258

    申请日:2019-03-06

    摘要: A deep neural network for metal artifact reduction is described. A method for computed tomography (CT) metal artifact reduction (MAR) includes generating, by a projection completion circuitry, an intermediate CT image data based, at least in part, on input CT projection data. The intermediate CT image data is configured to include relatively fewer artifacts than an uncorrected CT image reconstructed from the input CT projection data. The method further includes generating, by an artificial neural network (ANN), CT output image data based, at least in part, on the intermediate CT image data. The CT output image data is configured to include relatively fewer artifacts compared to the intermediate CT image data. The method may further include generating, by detail image circuitry, detail CT image data based, at least in part, on input CT image data. The CT output image data is generated based, at least in part, on the detail CT image data.

    Attenuation map reconstruction from TOF PET data

    公开(公告)号:US10537299B2

    公开(公告)日:2020-01-21

    申请号:US15579394

    申请日:2016-06-06

    摘要: Systems and methods for determining an attenuation sinogram for a time-of-flight (TOF) positron emission tomography (PET) scan using only TOF PET data, and including use of the total amount of tracer provided to the subject of the TOF PET scan, are provided. The total amount of injected tracer can be used to determine the otherwise unknown constant shift present when an attenuation sinogram is estimated using the gradient of the attenuation sinogram. The attenuation sinogram can therefore be accurately and stably determined without any additional knowledge on the attenuation sinogram or map.

    Image reconstruction method for computed tomography

    公开(公告)号:US11423591B2

    公开(公告)日:2022-08-23

    申请号:US16481298

    申请日:2017-05-23

    IPC分类号: G06T11/00 A61B6/03

    摘要: Systems and methods for reconstructing images for computed tomography are provided. Image reconstruction can be based on a realistic polychromatic physical model, and can include use of both an analytical algorithm and a single-variable optimization method. The optimization method can be used to solve the non-linear polychromatic X-ray integral model in the projection domain, resulting in an accurate decomposition for sinograms of two physical basis components.

    Stationary in-vivo grating-enabled micro-CT architecture (sigma)

    公开(公告)号:US11382574B2

    公开(公告)日:2022-07-12

    申请号:US16761543

    申请日:2018-11-06

    IPC分类号: A61B6/03 A61B6/00 G01N23/041

    摘要: A stationary in-vivo grating-enabled micro-CT (computed tomography) architecture (SIGMA) system includes CT scanner control circuitry and a number of imaging chains. Each imaging chain includes an x-ray source array, a phase grating, an analyzer grating and a detector array. Each imaging chain is stationary and each x-ray source array includes a plurality of x-ray source elements. Each imaging chain has a centerline, the centerlines of the number of imaging chains intersect at a center point and a first angle between the centerlines of a first adjacent pair of imaging chains equals a second angle between the centerlines of a second adjacent pair of imaging chains. A plurality of selected x-ray source elements of a first x-ray source array is configured to emit a plurality of x-ray beams in a multiplexing fashion.

    Tomographic image reconstruction via machine learning

    公开(公告)号:US10970887B2

    公开(公告)日:2021-04-06

    申请号:US16312704

    申请日:2017-06-26

    IPC分类号: G06T11/00 G06N20/00 G06N3/08

    摘要: Tomographic/tomosynthetic image reconstruction systems and methods in the framework of machine learning, such as deep learning, are provided. A machine learning algorithm can be used to obtain an improved tomographic image from raw data, processed data, or a preliminarily reconstructed intermediate image for biomedical imaging or any other imaging purpose. In certain cases, a single, conventional, non-deep-learning algorithm can be used on raw imaging data to obtain an initial image, and then a deep learning algorithm can be used on the initial image to obtain a final reconstructed image. All machine learning methods and systems for tomographic image reconstruction are covered, except for use of a single shallow network (three layers or less) for image reconstruction.

    METHODS AND SYSTEMS FOR STATIONARY COMPUTED TOMOGRAPHY

    公开(公告)号:US20190261930A1

    公开(公告)日:2019-08-29

    申请号:US16347828

    申请日:2017-11-07

    IPC分类号: A61B6/03 A61B5/055 A61B6/00

    摘要: Methods and systems for stationary computed tomography are disclosed. The methods and systems include a gantry having alternating x-ray sources and x-ray detectors that are stationary during operation of the system. The gantry and pairs of x-ray sources and detectors substantially surrounds an object positioned inside the gantry during operation of the system. Dynamically adjustable collimators are positioned between the x-ray sources and the object. Each of the x-ray sources projects an x-ray beam through the collimators and through the object and the x-ray detectors receive the x-ray beam. The x-ray detectors include means for converting the x-ray beam to raw image data. One or more microprocessors control the x-ray sources and the process raw image data. A data storage device stores instructions, which upon execution by the microprocessor, control the x-ray sources and process the raw image data by converting the raw image data to a digital image.