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    SYSTEM AND METHOD FOR STOCHASTIC DT-MRI CONNECTIVITY MAPPING ON THE GPU
    1.
    发明申请
    SYSTEM AND METHOD FOR STOCHASTIC DT-MRI CONNECTIVITY MAPPING ON THE GPU 失效
    用于在GPU上进行DT-MRI连接映射的系统和方法

    公开(公告)号：US20080109171A1

    公开(公告)日：2008-05-08

    申请号：US11843102

    申请日：2007-08-22

    申请人： Tim McGraw ,  Mariappan S. Nadar

    发明人： Tim McGraw ,  Mariappan S. Nadar

    IPC分类号： G01N33/48 ,  G01N24/08

    CPC分类号： G01R33/56341 ,  G01R33/5608

    摘要： A graphics processing unit implemented method for fiber tract mapping from diffusion tensor magnetic resonance imaging data includes providing a diffusion tensor magnetic resonance brain image volume, initializing a set of fiber positions in a 3D set of points, fiber displacements, and a posterior distribution for an updated fiber displacement in terms of the initial displacements and diffusion tensors, randomly sampling a set of updated fiber displacements from said posterior distribution, computing a new set of fiber positions from said initial fiber positions and said updated fiber displacements, wherein a fiber path comprises a set of fiber points connected by successive fiber displacements, accumulating connectivity values in each point of said 3D set of points by additive alpha-blending a scaled value if a fiber path has passed through a point and adding zero if not, and rendering said connectivity values.

    摘要翻译： 用于从扩散张量磁共振成像数据进行纤维束映射的图形处理单元实现方法包括提供扩散张量磁共振脑图像体积，初始化3D集合中的一组光纤位置，光纤位移和后验分布 在初始位移和扩散张量方面更新的光纤位移，从所述后验分布随机采样一组更新的光纤位移，从所述初始光纤位置和所述更新的光纤位移计算新的光纤位置集合，其中光纤路径包括 通过连续的光纤位移连接的一组光纤点，如果光纤路径已经通过一个点并且如果不是，则加上零，则通过加法阿尔法混合所述3D集合点的每个点中的连接值来累加连接值，并且使所述连接值 。

    System and method for stochastic DT-MRI connectivity mapping on the GPU
    2.
    发明授权
    System and method for stochastic DT-MRI connectivity mapping on the GPU 失效
    GPU上随机DT-MRI连通性映射的系统和方法

    公开(公告)号：US07672790B2

    公开(公告)日：2010-03-02

    申请号：US11843102

    申请日：2007-08-22

    申请人： Tim McGraw ,  Mariappan S. Nadar

    发明人： Tim McGraw ,  Mariappan S. Nadar

    IPC分类号： G01N24/00 ,  G01V3/14

    CPC分类号： G01R33/56341 ,  G01R33/5608

    摘要： A graphics processing unit implemented method for fiber tract mapping from diffusion tensor magnetic resonance imaging data includes providing a diffusion tensor magnetic resonance brain image volume, initializing a set of fiber positions in a 3D set of points, fiber displacements, and a posterior distribution for an updated fiber displacement in terms of the initial displacements and diffusion tensors, randomly sampling a set of updated fiber displacements from said posterior distribution, computing a new set of fiber positions from said initial fiber positions and said updated fiber displacements, wherein a fiber path comprises a set of fiber points connected by successive fiber displacements, accumulating connectivity values in each point of said 3D set of points by additive alpha-blending a scaled value if a fiber path has passed through a point and adding zero if not, and rendering said connectivity values.

    摘要翻译： 用于从扩散张量磁共振成像数据进行纤维束映射的图形处理单元实现方法包括提供扩散张量磁共振脑图像体积，初始化3D集合中的一组光纤位置，光纤位移和后验分布 在初始位移和扩散张量方面更新的光纤位移，从所述后验分布随机采样一组更新的光纤位移，从所述初始光纤位置和所述更新的光纤位移计算新的光纤位置集合，其中光纤路径包括 通过连续的光纤位移连接的一组光纤点，如果光纤路径已经通过一个点并且如果不是，则加上零，则通过加法阿尔法混合所述3D集合点的每个点中的连接值来累加连接值，并且使所述连接值 。

    Generalized Approximate Message Passing Algorithms for Sparse Magnetic Resonance Imaging Reconstruction
    3.
    发明申请
    Generalized Approximate Message Passing Algorithms for Sparse Magnetic Resonance Imaging Reconstruction 有权
    广义近似消息传递算法用于稀疏磁共振成像重建

    公开(公告)号：US20160247299A1

    公开(公告)日：2016-08-25

    申请号：US14630712

    申请日：2015-02-25

    申请人： Jin Tan ,  Boris Mailhe ,  Qiu Wang ,  Mariappan S. Nadar

    发明人： Jin Tan ,  Boris Mailhe ,  Qiu Wang ,  Mariappan S. Nadar

    IPC分类号： G06T11/00 ,  G06T7/00

    CPC分类号： G06T11/003 ,  G01R33/5611 ,  G06T7/0012 ,  G06T2207/10088

    摘要： A method for reconstructing magnetic resonance imaging data includes acquiring a measurement dataset using a magnetic resonance imaging device and determining an estimated image dataset based on the measurement dataset. An iterative reconstruction process is performed to refine the estimated image dataset. Each iteration of the iterative reconstruction process comprises: updating the measurement dataset and a sparse coefficient dataset based on the estimated image dataset and a plurality of belief propagation terms, incorporating a noise prior dataset into the measurement dataset, incorporating a sparsity prior dataset into the sparse coefficient dataset, updating the plurality of belief propagation terms based on the measurement dataset and the sparsity prior dataset, and updating the estimated image dataset based on the plurality of belief propagation terms. A reconstructed image and confidence map are generated using the estimated image dataset.

    摘要翻译： 一种用于重建磁共振成像数据的方法包括使用磁共振成像装置获取测量数据集，并且基于测量数据集确定估计的图像数据集。 执行迭代重建过程以细化估计的图像数据集。 迭代重建过程的每次迭代包括：基于估计的图像数据集和多个置信传播项更新测量数据集和稀疏系数数据集，将噪声预先数据集合并入测量数据集，将稀疏性先验数据集合并入稀疏 系数数据集，基于测量数据集和稀疏性先验数据集更新多个置信传播项，以及基于多个置信传播项更新估计图像数据集。 使用估计的图像数据集生成重建图像和置信图。

    Zero communication block partitioning
    4.
    发明授权
    Zero communication block partitioning 有权
    零通信块分区

    公开(公告)号：US09286648B2

    公开(公告)日：2016-03-15

    申请号：US13950535

    申请日：2013-07-25

    申请人： Mariappan S. Nadar

    发明人： Alban Lefebvre ,  Axel Loewe ,  Mariappan S. Nadar ,  Jun Liu

    IPC分类号： G06K9/54 ,  G06T1/20 ,  G01R33/56

    CPC分类号： G06T1/20 ,  G01R33/5608

    摘要： A computer-implemented method for calculating a multi-dimensional wavelet transform in an image processing system comprising a plurality of computation units includes receiving multi-dimensional image data. An overlap value corresponding to a number of non-zero filter coefficients associated with the multi-dimensional wavelet transform is identified. Then the multi-dimensional image data is divided into a plurality of multi-dimensional arrays, wherein the multi-dimensional arrays overlap in each dimension by a number of pixels equal to the overlap value. A multi-dimensional wavelet transform is calculated for each multi-dimensional array, in parallel, across the plurality of computation units.

    摘要翻译： 一种用于在包括多个计算单元的图像处理系统中计算多维小波变换的计算机实现方法包括接收多维图像数据。 识别与多维小波变换相关联的非零滤波器系数的数量对应的重叠值。 然后，多维图像数据被分成多个多维阵列，其中多维阵列在每个维度上与等于重叠值的像素的数量重叠。 在多个计算单元中并行计算每个多维阵列的多维小波变换。

    MULTI-STAGE MAGNETIC RESONANCE RECONSTRUCTION FOR PARALLEL IMAGING APPLICATIONS
    5.
    发明申请
    MULTI-STAGE MAGNETIC RESONANCE RECONSTRUCTION FOR PARALLEL IMAGING APPLICATIONS 有权
    并行成像应用的多级磁共振重建

    公开(公告)号：US20140133724A1

    公开(公告)日：2014-05-15

    申请号：US14054003

    申请日：2013-10-15

    申请人： Jun Liu ,  Zhili Yang ,  Mariappan S. Nadar ,  Nirmal Janardhanan ,  Michael Zenge ,  Edgar Mueller ,  Qiu Wang ,  Axel Loewe

    发明人： Jun Liu ,  Zhili Yang ,  Mariappan S. Nadar ,  Nirmal Janardhanan ,  Michael Zenge ,  Edgar Mueller ,  Qiu Wang ,  Axel Loewe

    IPC分类号： G06T11/00

    CPC分类号： G01R33/5611

    摘要： A computer-implemented method for reconstruction of a magnetic resonance image includes acquiring a first incomplete k-space data set comprising a plurality of first k-space lines spaced according to an acceleration factor and one or more calibration lines. A parallel imaging reconstruction technique is applied to the first incomplete k-space data to determine a plurality of second k-space lines not included in the first incomplete k-space data set, thereby yielding a second incomplete k-space data set. Then, the parallel imaging reconstruction technique is applied to the second incomplete k-space data to determine a plurality of third k-space lines not included in the second incomplete k-space data, thereby yielding a complete k-space data set.

    摘要翻译： 用于重建磁共振图像的计算机实现的方法包括获取包括根据加速因子和一个或多个校准线间隔开的多个第一k空间线的第一不完整k空间数据集。 将并行成像重建技术应用于第一不完整k空间数据以确定未包括在第一不完整k空间数据集中的多个第二k空间线，从而产生第二不完整k空间数据集。 然后，将并行成像重构技术应用于第二不完整k空间数据，以确定不包括在第二不完整k空间数据中的多个第三k空间线，从而产生完整的k空间数据集。

    Generalized approximate message passing algorithms for sparse magnetic resonance imaging reconstruction
    8.
    发明授权
    Generalized approximate message passing algorithms for sparse magnetic resonance imaging reconstruction 有权
    用于稀疏磁共振成像重建的广义近似消息传递算法

    公开(公告)号：US09542761B2

    公开(公告)日：2017-01-10

    申请号：US14630712

    申请日：2015-02-25

    申请人： Jin Tan ,  Boris Mailhe ,  Qiu Wang ,  Mariappan S. Nadar

    发明人： Jin Tan ,  Boris Mailhe ,  Qiu Wang ,  Mariappan S. Nadar

    IPC分类号： G06K9/00 ,  G06T11/00 ,  G06T7/00 ,  A61B6/00

    CPC分类号： G06T11/003 ,  G01R33/5611 ,  G06T7/0012 ,  G06T2207/10088

    摘要： A method for reconstructing magnetic resonance imaging data includes acquiring a measurement dataset using a magnetic resonance imaging device and determining an estimated image dataset based on the measurement dataset. An iterative reconstruction process is performed to refine the estimated image dataset. Each iteration of the iterative reconstruction process comprises: updating the measurement dataset and a sparse coefficient dataset based on the estimated image dataset and a plurality of belief propagation terms, incorporating a noise prior dataset into the measurement dataset, incorporating a sparsity prior dataset into the sparse coefficient dataset, updating the plurality of belief propagation terms based on the measurement dataset and the sparsity prior dataset, and updating the estimated image dataset based on the plurality of belief propagation terms. A reconstructed image and confidence map are generated using the estimated image dataset.

    摘要翻译： 一种用于重建磁共振成像数据的方法包括使用磁共振成像装置获取测量数据集，并且基于测量数据集确定估计的图像数据集。 执行迭代重建过程以细化估计的图像数据集。 迭代重建过程的每次迭代包括：基于估计的图像数据集和多个置信传播项更新测量数据集和稀疏系数数据集，将噪声预先数据集合并入测量数据集，将稀疏性先验数据集合并入稀疏 系数数据集，基于测量数据集和稀疏性先验数据集更新多个置信传播项，以及基于多个置信传播项更新估计图像数据集。 使用估计的图像数据集生成重建图像和置信图。

    MRI reconstruction with motion-dependent regularization
    9.
    发明授权
    MRI reconstruction with motion-dependent regularization 有权
    MRI重建与运动相关正则化

    公开(公告)号：US09482732B2

    公开(公告)日：2016-11-01

    申请号：US14065498

    申请日：2013-10-29

    申请人： Nicolas Chesneau ,  Nirmal Janardhanan ,  Jun Liu ,  Mariappan S. Nadar ,  Qiu Wang ,  Zhili Yang

    发明人： Nicolas Chesneau ,  Nirmal Janardhanan ,  Jun Liu ,  Mariappan S. Nadar ,  Qiu Wang ,  Zhili Yang

    IPC分类号： G06K9/00 ,  G01R33/563 ,  G01R33/48 ,  G01R33/565 ,  G01R33/561

    CPC分类号： G01R33/56308 ,  G01R33/4824 ,  G01R33/5611 ,  G01R33/56509

    摘要： A method of image reconstruction for a magnetic resonance imaging (MRI) system includes obtaining k-space scan data captured by the MRI system, the k-space scan data being representative of an undersampled region over time, iteratively reconstructing preliminary dynamic images for the undersampled region from the k-space scan data via optimization of a first instance of a minimization problem, the minimization problem including a regularization term weighted by a weighting parameter array, generating a motion determination indicative of an extent to which each location of the undersampled region exhibits motion over time based on the preliminary dynamic images, and iteratively reconstructing motion-compensated dynamic images for the region from the k-space scan data via optimization of a second instance of the minimization problem, the second instance having the weighting parameter array altered as a function of the motion determination.

    摘要翻译： 用于磁共振成像（MRI）系统的图像重建方法包括获得由MRI系统捕获的k空间扫描数据，k空间扫描数据代表欠采样区域随着时间的推移，迭代地重建欠采样的初步动态图像 该最小化问题包括由加权参数阵列加权的正则化项，产生表示欠采样区域的每个位置显示的程度的运动确定 基于初步动态图像的时间运动，以及经由最小化问题的第二实例的优化从k空间扫描数据迭代地重建该区域的运动补偿动态图像，将具有加权参数阵列的第二实例改变为 运动确定的功能。