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    Unifying reconstruction and motion estimation in first pass cardiac perfusion imaging
    1.
    发明授权
    Unifying reconstruction and motion estimation in first pass cardiac perfusion imaging 有权
    一次心脏灌注显像统一重建和运动估计

    公开(公告)号：US08553964B2

    公开(公告)日：2013-10-08

    申请号：US13273410

    申请日：2011-10-14

    申请人： Christophe Chefd'hotel ,  Mathews Jacob ,  Sajan Goud Lingala ,  Mariappan S. Nadar ,  Li Zhang

    发明人： Christophe Chefd'hotel ,  Mathews Jacob ,  Sajan Goud Lingala ,  Mariappan S. Nadar ,  Li Zhang

    IPC分类号： G06K9/00 ,  A61B5/00 ,  A61B8/00

    CPC分类号： G01R33/56366 ,  G01R33/5611 ,  G01R33/56509

    摘要： Methods and a system to unify reconstruction and motion estimation steps in first pass cardiac perfusion MRI include a global objective function that meets data consistency, spatial smoothness, motion and contrast dynamics constraints. The global objective decomposed into simpler sub-problems which include low pass filtering of a deformed object, TV shrinkage, analytical Fourier replacement and an l2 minimizing problem. A registration tool based on the local cross-correlation similarity measure and enabled to perform both rigid and flexile deformations, is applied. Registration parameters are tuned by rigid, semi rigid and flexible models at different stages of iterations. A system to perform the methods is also disclosed.

    摘要翻译： 在第一次心脏灌注MRI中统一重建和运动估计步骤的方法和系统包括满足数据一致性，空间平滑度，运动和对比度动力学约束的全局目标函数。 全局目标分解为更简单的子问题，包括变形对象的低通滤波，电视收缩，分析​​傅立叶置换和最小二乘问题。 应用基于局部互相关相似性测量并且能够执行刚性和柔性变形的注册工具。 注册参数通过刚性，半刚性和柔性模型在不同的迭代阶段进行调整。 还公开了执行方法的系统。

    UNIFYING RECONSTRUCTION AND MOTION ESTIMATION IN FIRST PASS CARDIAC PERFUSION IMAGING
    2.
    发明申请
    UNIFYING RECONSTRUCTION AND MOTION ESTIMATION IN FIRST PASS CARDIAC PERFUSION IMAGING 有权
    第一次心脏灌注成像中的统一重建和运动估计

    公开(公告)号：US20120148128A1

    公开(公告)日：2012-06-14

    申请号：US13273410

    申请日：2011-10-14

    申请人： Christophe Chefd'hotel ,  Mathews Jacob ,  Sajan Goud Lingala ,  Mariappan S. Nadar ,  Li Zhang

    发明人： Christophe Chefd'hotel ,  Mathews Jacob ,  Sajan Goud Lingala ,  Mariappan S. Nadar ,  Li Zhang

    IPC分类号： G06K9/00

    CPC分类号： G01R33/56366 ,  G01R33/5611 ,  G01R33/56509

    摘要： Methods and a system to unify reconstruction and motion estimation steps in first pass cardiac perfusion MRI include a global objective function that meets data consistency, spatial smoothness, motion and contrast dynamics constraints. The global objective decomposed into simpler sub-problems which include low pass filtering of a deformed object, TV shrinkage, analytical Fourier replacement and an l2 minimizing problem. A registration tool based on the local cross-correlation similarity measure and enabled to perform both rigid and flexile deformations, is applied. Registration parameters are tuned by rigid, semi rigid and flexible models at different stages of iterations. A system to perform the methods is also disclosed.

    摘要翻译： 在第一次心脏灌注MRI中统一重建和运动估计步骤的方法和系统包括满足数据一致性，空间平滑度，运动和对比度动力学约束的全局目标函数。 全局目标分解为更简单的子问题，包括变形对象的低通滤波，电视收缩，分析​​傅立叶置换和最小二乘问题。 应用基于局部互相关相似性测量并且能够执行刚性和柔性变形的注册工具。 注册参数通过刚性，半刚性和柔性模型在不同的迭代阶段进行调整。 还公开了执行方法的系统。

    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。

    Visualizing brain network connectivity
    8.
    发明授权
    Visualizing brain network connectivity 有权
    可视化大脑网络连接

    公开(公告)号：US09519981B2

    公开(公告)日：2016-12-13

    申请号：US13539863

    申请日：2012-07-02

    申请人： Sandra Sudarsky ,  Mariappan S. Nadar ,  Shanhui Sun ,  Alban Lefebvre ,  Bernhard Geiger

    发明人： Sandra Sudarsky ,  Mariappan S. Nadar ,  Shanhui Sun ,  Alban Lefebvre ,  Bernhard Geiger

    IPC分类号： G09G5/02 ,  G06T11/20 ,  A61B5/00

    CPC分类号： G06T11/206 ,  A61B5/0042 ,  A61B5/4064

    摘要： A method for visualizing brain connectivity includes receiving image data including molecular diffusion of brain tissue, constructing a tree data structure from the image data, wherein the tree data structure comprises a plurality of network nodes, wherein each network node is connected to a root of the tree data structure, rendering a ring of a radial layout depicting the tree data structure, wherein a plurality of vertices may be traversed from the top to the bottom, duplicating at least one control point for spline edges sharing a common ancestor, and bundling spline edges by applying a global strength parameter β.

    摘要翻译： 用于可视化脑连通性的方法包括接收包括脑组织的分子扩散的图像数据，从图像数据构建树数据结构，其中树数据结构包括多个网络节点，其中每个网络节点连接到 树形数据结构，呈现描绘树状数据结构的径向布局的环，其中可以从顶部到底部遍历多个顶点，复制共享共同祖先的花键边缘的至少一个控制点，以及捆绑样条边 通过应用全局强度参数β。