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    SYSTEM AND METHOD FOR SPARSE IMAGE RECONSTRUCTION
    2.
    发明申请
    SYSTEM AND METHOD FOR SPARSE IMAGE RECONSTRUCTION 审中-公开

    公开(公告)号：US20190266761A1

    公开(公告)日：2019-08-29

    申请号：US15907797

    申请日：2018-02-28

    Applicant: GENERAL ELECTRIC COMPANY

    Inventor： Itzik Malkiel ,  Sangtae Ahn ,  Christopher Judson Hardy

    IPC: G06T11/00 ,  G01R33/561 ,  G01R33/48

    Abstract: A method for sparse image reconstruction includes acquiring coil data from a magnetic resonance imaging device. The coil data includes undersampled k-space data corresponding to a subject. The method further includes processing the coil data using an image reconstruction technique to generate an initial undersampled image. The method also includes generating a reconstructed image based on the coil data, the initial undersampled image, and a plurality of iterative blocks of a flared network. A first iterative block of the flared network receives the initial undersampled image. Each of the plurality of iterative blocks includes a data consistency unit and a regularization unit and the iterative blocks are connected both by direct connections from one iterative block to the following iterative block and by a plurality of dense skip connections to non-adjacent iterative blocks. The flared network is based on a neural network trained using previously acquired coil data.

    Systems and methods for partial volume correction in PET penalized-likelihood image reconstruction
    3.
    发明授权
    Systems and methods for partial volume correction in PET penalized-likelihood image reconstruction 有权
    PET惩罚似然图像重建中部分体积校正的系统和方法

    公开(公告)号：US09256967B2

    公开(公告)日：2016-02-09

    申请号：US13667757

    申请日：2012-11-02

    Applicant: General Electric Company

    Inventor： Sangtae Ahn ,  Evren Asma ,  Ravindra Manjeshwar ,  Steven Ross

    IPC: G06K9/00 ,  G06T11/00

    CPC classification number: G06T11/008

    Abstract: A computer-implemented method for partial volume correction in Positron Emission Tomography (PET) image reconstruction includes receiving emission data related to an activity distribution, reconstructing the activity distribution from the emission data by maximizing a penalized-likelihood objective function to produce a reconstructed PET image, quantifying an activity concentration in a region of interest of the reconstructed PET image to produce an uncorrected quantitation, and correcting the uncorrected quantitation based on a pre-calculated contrast recovery coefficient value to account for a partial volume error in the uncorrected quantitation.

    Abstract translation: 用于正电子发射断层扫描（PET）图像重建中的部分体积校正的计算机实现的方法包括接收与活动分布相关的发射数据，通过最大化惩罚似然度目标函数来重建来自发射数据的活动分布，以产生重建的PET图像 量化重构的PET图像的感兴趣区域中的活性浓度以产生未校正的定量，并且基于预计算的对比度恢复系数值来校正未校正的定量，以解决未校正的定量中的部分体积误差。

    Systems and methods for imaging with anisotropic voxels
    4.
    发明授权
    Systems and methods for imaging with anisotropic voxels 有权

    公开(公告)号：US10482634B2

    公开(公告)日：2019-11-19

    申请号：US15792363

    申请日：2017-10-24

    Applicant: General Electric Company

    Inventor： Nitin Jain ,  Sangtae Ahn ,  Steven Ross

    IPC: G06T11/00 ,  G06T3/40 ,  A61B6/03

    Abstract: An imaging system is provided that includes at least one detector configured to acquire imaging information, a processing unit, and a display unit. The processing unit is operably coupled to the at least one detector, and is configured to reconstruct an image using the imaging information. The image is organized into voxels having non-uniform dimensions. The processing unit is configured to perform a penalized likelihood (PL) image reconstruction using the imaging information. The PL image reconstruction includes a penalty function. Performing the penalty function includes interpolating a voxel size in at least one dimension from an original size to an interpolated size before determining a penalty function, determining the penalty function using the interpolated size to provide an initial penalty, interpolating the initial penalty to the original size to provide a modified penalty, and applying the modified penalty in the PL image reconstruction.

    SYSTEM AND METHOD RECONSTRUCTING A NUCLEAR MEDICINE IMAGE USING DEFORMED ATTENUATION IMAGE
    7.
    发明申请
    SYSTEM AND METHOD RECONSTRUCTING A NUCLEAR MEDICINE IMAGE USING DEFORMED ATTENUATION IMAGE 有权
    系统和方法使用变形的衰减图像重建核医学图像

    公开(公告)号：US20150117733A1

    公开(公告)日：2015-04-30

    申请号：US14063473

    申请日：2013-10-25

    Applicant: General Electric Company

    Inventor： Ravindra Mohan Manjeshwar ,  Sangtae Ahn ,  Kris Thielemans

    IPC: G06T11/00

    CPC classification number: G06T11/003 ,  G06T11/005

    Abstract: According to some embodiments, an emission tomography scanner may acquire emission scan data. One or more anatomical images may be generated using an anatomical imaging system, and the anatomical images may be processed to obtain an initial attenuation image. An emission image and a corrected attenuation image may be jointly reconstructed from the acquired emission scan data, the corrected attenuation image representing a deformation of the initial attenuation image. A final reconstructed emission image may then be calculated based on the reconstructed emission image and/or the corrected attenuation image. The final reconstructed emission image may then be stored in a data storage system and/or displayed on a display system.

    Abstract translation: 根据一些实施例，发射断层扫描仪可以获取发射扫描数据。 可以使用解剖学成像系统生成一个或多个解剖图像，并且可以处理解剖图像以获得初始衰减图像。 发射图像和经校正的衰减图像可以从获取的发射扫描数据共同重建，校正的衰减图像表示初始衰减图像的变形。 然后可以基于重构的发射图像和/或校正的衰减图像来计算最终重建的发射图像。 然后可以将最终重构的发射图像存储在数据存储系统中和/或显示在显示系统上。

    JOINT ESTIMATION OF ATTENUATION AND ACTIVITY INFORMATION USING EMISSION DATA
    8.
    发明申请
    JOINT ESTIMATION OF ATTENUATION AND ACTIVITY INFORMATION USING EMISSION DATA 审中-公开
    使用排放数据联合评估衰减和活动信息

    公开(公告)号：US20150065854A1

    公开(公告)日：2015-03-05

    申请号：US13665551

    申请日：2012-10-31

    Applicant: General Electric Company

    Inventor： Sangtae Ahn ,  Ravindra Mohan Manjeshwar

    IPC: A61B6/00 ,  G01R33/385 ,  A61B6/03 ,  G01R33/48

    CPC classification number: A61B6/4417 ,  A61B6/037 ,  A61B6/5247 ,  G01R33/481

    Abstract: Methods, systems and non-transitory computer readable media for imaging are disclosed. Emission projection data corresponding to a target region of a subject is acquired using an emission tomography system. Additionally, one or more magnetic resonance images of the target region are generated using a magnetic resonance imaging system operatively coupled to the emission tomography system. A partially-determined attenuation map is determined by identifying one or more regions in the partially-determined attenuation map with a designated confidence level based on the magnetic resonance images. Further, a complete attenuation map and/or a complete activity map is reconstructed from the emission projection data using the partially-determined attenuation map as a constraint. One or more images corresponding to the target region are then generated based on the partially-determined attenuation map, the complete attenuation map and/or the complete activity map.

    Abstract translation: 公开了用于成像的方法，系统和非暂时计算机可读介质。 使用发射断层摄影系统获取与被检体的目标区域对应的发射投影数据。 另外，使用可操作地耦合到发射断层摄影系统的磁共振成像系统来产生目标区域的一个或多个磁共振图像。 通过基于磁共振图像以指定的置信水平识别部分确定的衰减图中的一个或多个区域来确定部分确定的衰减图。 此外，使用部分确定的衰减图作为约束，从发射投影数据重建完整的衰减图和/或完整的活动图。 然后基于部分确定的衰减图，完整的衰减图和/或完整的活动图来产生对应于目标区域的一个或多个图像。

    Reconstruction of MR image data
    9.
    发明授权
    Reconstruction of MR image data 有权

    公开(公告)号：US11307278B2

    公开(公告)日：2022-04-19

    申请号：US16733037

    申请日：2020-01-02

    Applicant: General Electric Company

    Inventor： Christopher Judson Hardy ,  Sangtae Ahn

    IPC: G01R33/56 ,  G06T5/50 ,  G06T11/00 ,  G06N20/00

    Abstract: The subject matter discussed herein relates to a fast magnetic resonance imaging (MRI) method to suppress fine-line artifact in Fast-Spin-Echo (FSE) images reconstructed with a deep-learning network. The network is trained using fully sampled NEX=2 (Number of Excitations equals to 2) data. In each case, the two excitations are combined to generate fully sampled ground-truth images with no fine-line artifact, which are used for comparison with the network generated image in the loss function. However, only one of the excitations is retrospectively undersampled and inputted into the network during training. In this way, the network learns to remove both undersampling and fine-line artifacts. At inferencing, only NEX=1 undersampled data are acquired and reconstructed.

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