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1. 发明授权

US08948480B2 Image reconstruction using redundant Haar wavelets 有权
标题翻译：使用冗余哈尔小波的图像重建
公开(公告)号：US08948480B2
公开(公告)日：2015-02-03
申请号：US13616484
申请日：2012-09-14
申请人： Jun Liu , Jeremy Rapin , Alban Lefebvre , Mariappan S. Nadar , Ti-chiun Chang , Michael Zenge , Edgar Müller
发明人： Jun Liu , Jeremy Rapin , Alban Lefebvre , Mariappan S. Nadar , Ti-chiun Chang , Michael Zenge , Edgar Müller
IPC分类号： G06K9/00 , G06T11/00 , G01R33/561 , G01R33/48 , G01R33/563
CPC分类号： G06T11/005 , G01R33/482 , G01R33/5611 , G01R33/5619 , G01R33/56308
摘要： A method for image reconstruction includes receiving under-sampled k-space data, determining a data fidelity term of a first image of the under-sampled k-space data in view of a second image of the under-sampled k-space data, wherein a time component separated the first image and the second image, determining a spatial penalization on redundant Haar wavelet coefficients of the first image in view of the second image, and optimizing the first image according the data fidelity term and the spatial penalization, wherein the spatial penalization selectively penalizes temporal coefficients and an optimized image of the first image is output.
摘要翻译：一种用于图像重构的方法包括：接收未被采样的k空间数据，鉴于未被采样的k空间数据的第二图像，确定未被采样的k空间数据的第一图像的数据保真度项，其中时间分量分离第一图像和第二图像，根据第二图像确定第一图像的冗余Haar小波系数的空间惩罚，以及根据数据保真度项和空间惩罚优化第一图像，其中空间惩罚性选择性地惩罚时间系数，并且输出第一图像的优化图像。

2. 发明申请

US20130121554A1 IMAGE RECONSTRUCTION USING REDUNDANT HAAR WAVELETS 有权
标题翻译：使用冗余HAAR波段的图像重建
公开(公告)号：US20130121554A1
公开(公告)日：2013-05-16
申请号：US13616484
申请日：2012-09-14
申请人： Jun Liu , Jeremy Rapin , Alban Lefebvre , Mariappan S. Nadar , Ti-chiun Chang , Michael Zenge , Edgar Müller
发明人： Jun Liu , Jeremy Rapin , Alban Lefebvre , Mariappan S. Nadar , Ti-chiun Chang , Michael Zenge , Edgar Müller
IPC分类号： G06T11/00
CPC分类号： G06T11/005 , G01R33/482 , G01R33/5611 , G01R33/5619 , G01R33/56308
摘要： A method for image reconstruction includes receiving under-sampled k-space data, determining a data fidelity term of a first image of the under-sampled k-space data in view of a second image of the under-sampled k-space data, wherein a time component separated the first image and the second image, determining a spatial penalization on redundant Haar wavelet coefficients of the first image in view of the second image, and optimizing the first image according the data fidelity term and the spatial penalization, wherein the spatial penalization selectively penalizes temporal coefficients and an optimized image of the first image is output.
摘要翻译：一种用于图像重构的方法包括：接收未被采样的k空间数据，鉴于未被采样的k空间数据的第二图像，确定未被采样的k空间数据的第一图像的数据保真度项，其中时间分量分离第一图像和第二图像，根据第二图像确定第一图像的冗余Haar小波系数的空间惩罚，以及根据数据保真度项和空间惩罚优化第一图像，其中空间惩罚性选择性地惩罚时间系数，并且输出第一图像的优化图像。

3. 发明授权

US09632156B2 Efficient redundant haar minimization for parallel MRI reconstruction 有权
公开(公告)号：US09632156B2
公开(公告)日：2017-04-25
申请号：US13717842
申请日：2012-12-18
申请人： Jun Liu , Jeremy Rapin , Alban Lefebvre , Mariappan S. Nadar , Ti-chiun Chang
发明人： Jun Liu , Jeremy Rapin , Alban Lefebvre , Mariappan S. Nadar , Ti-chiun Chang
IPC分类号： G06K9/00 , G01R33/48 , G06T7/00 , G01R33/561
CPC分类号： G01R33/4818 , G01R33/5611 , G06T7/0012 , G06T2207/10088
摘要： A method for parallel magnetic resonance imaging (MRI) reconstruction of digital images includes providing a set of acquired k-space MR image data v, a redundant Haar wavelet matrix W satisfying WTW=I, wherein I is an identity matrix, a regularization parameter λ≧0, and a counter limit k, initializing a variable z0=Wv, and intermediate quantities p0=q0=0, calculating yi=arg minz½∥z−(pi+zi)∥22+λ∥z∥1 for 0≦i≦k, wherein z denotes values of an MR image sought to be reconstructed, updating pi+1=(pi+zi)−yi, updating zi+1=arg minz½∥z−(qi+zi)∥22+g(z), wherein g ⁡ ( z ) = { 0 , z = WW T ⁢ z , + ∞ , otherwise ; and updating qi+1=(qi+yi)−zi−l, wherein x=WTz is a solution of min x ⁢ 1 2 ⁢  Wx - Wv  2 2 + λ ⁢  Wx  1 that specifies a reconstruction of the MR image.

4. 发明申请

US20130320974A1 EFFICIENT REDUNDANT HAAR MINIMIZATION FOR PARALLEL MRI RECONSTRUCTION 有权
标题翻译：有效的冗余HAAR最小化并行MRI重建
公开(公告)号：US20130320974A1
公开(公告)日：2013-12-05
申请号：US13717842
申请日：2012-12-18
申请人： Jun Liu , Jeremy Rapin , Alban Lefebvre , Mariappan S. Nadar , Ti-chiun Chang
发明人： Jun Liu , Jeremy Rapin , Alban Lefebvre , Mariappan S. Nadar , Ti-chiun Chang
IPC分类号： G01R33/48
CPC分类号： G01R33/4818 , G01R33/5611 , G06T7/0012 , G06T2207/10088
摘要： A method for parallel magnetic resonance imaging (MRI) reconstruction of digital images includes providing a set of acquired k-space MR image data v, a redundant Haar wavelet matrix W satisfying WTW=I, wherein I is an identity matrix, a regularization parameter λ≧0, and a counter limit k, initializing a variable z0=Wv, and intermediate quantities p0=q0=0, calculating yi=arg minz 1/2∥z−(pi+zi)∥22+λ∥z∥1 for 0≦i≦k, wherein z denotes values of an MR image sought to be reconstructed, updating pi+1=(pi+zi)−yi, updating zi+1=arg minz 1/2∥z−(qi+zi)∥22+g(z), wherein g  ( z ) = { 0 , z = WW T  z , + ∞ , otherwise ; and updating qi+1=(qi+yi)−zi−1, wherein x=WTz is a solution of min x  1 2   Wx - Wv  2 2 + λ   Wx  1 that specifies a reconstruction of the MR image.
摘要翻译：用于数字图像的并行磁共振成像（MRI）重建的方法包括提供一组获得的k空间MR图像数据v，满足WTW = I的冗余Haar小波矩阵W，其中I是单位矩阵，正则化参数λ > = 0，以及计数器极限k，初始化变量z0 = Wv，中间量p0 = q0 = 0，计算yi = arg minz 1 /2‖z-（pi + zi）‖22+lambda‖z‖1 对于0 @ i @ k，其中z表示寻求重建的MR图像的值，更新pi + 1 =（pi + zi）-yi，更新zi + 1 = arg minz 1 /2‖z-（qi + zi ）∥22+ g（z），其中g（z）= {0，z = WW T z z +∞，否则; 并且更新qi + 1 =（qi + yi）-zi-1，其中x = WTz是min x的解，W x -Wv2 2 +λ1， MR图像。

5. 发明申请

US20140088899A1 EIGEN-VECTOR APPROACH FOR COIL SENSITIVITY MAPS ESTIMATION 审中-公开
标题翻译：用于线圈灵敏度估计的EIGEN-VECTOR方法
公开(公告)号：US20140088899A1
公开(公告)日：2014-03-27
申请号：US14038958
申请日：2013-09-27
申请人： Jun Liu , Hui Xue , Marcel Dominik Nickel , Ti-chiun Chang , Mariappan S. Nadar , Alban Lefebvre , Edgar Mueller , Qiu Wang , Zhili Yang , Nirmal Janardhanan , Michael Zenge
发明人： Jun Liu , Hui Xue , Marcel Dominik Nickel , Ti-chiun Chang , Mariappan S. Nadar , Alban Lefebvre , Edgar Mueller , Qiu Wang , Zhili Yang , Nirmal Janardhanan , Michael Zenge
IPC分类号： G01R33/24
CPC分类号： G01R33/243 , G01R33/246 , G01R33/5611
摘要： A method for estimating a coil sensitivity map for a magnetic resonance (MR) image includes providing a matrix A of sliding blocks of a 3D image of coil calibration data, calculating a left singular matrix V∥ from a singular value decomposition of A corresponding to τ leading singular values, calculating P=V∥V∥H, calculating a matrix S that is an inverse Fourier transform of a zero-padded matrix P, and solving MHcr=(Sr)Hcr for cr, where cr is a vector of coil sensitivity maps for all coils at spatial location r, and M = ( ( 1 1 … 1 0 0 … 0 … … … 0 0 … 0 )  ( 0 0 … 0 1 1 … 1 … … … 0 0 … 0 )   …   ( 0 0 … 0 0 0 … 0 … … … 1 1 … 1 ) ) .
摘要翻译：用于估计磁共振（MR）图像的线圈灵敏度图的方法包括提供线圈校准数据的3D图像的滑动块的矩阵A，从对应于τ的A的奇异值分解计算左奇异矩阵V∥ 计算P = V || V | H，计算作为零填充矩阵P的傅立叶逆变换的矩阵S，求解cr的MHcr =（Sr）Hcr，其中cr是线圈灵敏度的向量对于空间位置r处的所有线圈的映射，M =（（1 1 ... 1 0 0 ... 0 ... ... 0 0 ... 0）（0 0 ... 0 1 1 ... 1 ... ... 0 0 ... 0） ...（0 0 ... 0 0 0 ... 0 ...... ... 1 1 ... 1））。

6. 发明申请

US20130289912A1 EIGEN-VECTOR APPROACH FOR COIL SENSITIVITY MAPS ESTIMATION 审中-公开
标题翻译：用于线圈灵敏度估计的EIGEN-VECTOR方法
公开(公告)号：US20130289912A1
公开(公告)日：2013-10-31
申请号：US13779999
申请日：2013-02-28
申请人： Jun Liu , Hui Xue , Marcel Dominik Nickel , Ti-chiun Chang , Mariappan S. Nadar , Alban Lefebvre , Edgar Mueller , Qiu Wang , Zhili Yang , Nirmal Janardhanan , Michael Zenge
发明人： Jun Liu , Hui Xue , Marcel Dominik Nickel , Ti-chiun Chang , Mariappan S. Nadar , Alban Lefebvre , Edgar Mueller , Qiu Wang , Zhili Yang , Nirmal Janardhanan , Michael Zenge
IPC分类号： G01R33/24
CPC分类号： G01R33/243 , G01R33/246 , G01R33/5611
摘要： A method for estimating a coil sensitivity map for a magnetic resonance (MR) image includes providing (61) a matrix A of sliding blocks of a 2D image of coil calibration data, calculating (62) a left singular matrix V∥ from a singular value decomposition of A corresponding to τ leading singular values, calculating (63) P=V∥V∥H, calculating (64) a matrix S that is an inverse Fourier transform of a zero-padded matrix P, and solving (65) MHcr=(Sr)Hcr for cr, where cr is a vector of coil sensitivity maps for all coils at spatial location r, and M  ( ( 1 1 … 1 0 0 … 0 … … … 0 0 … 0 )  ( 0 0 … 0 1 1 … 1 … … … 0 0 … 0 )   …   ( 0 0 … 0 0 0 … 0 … … … 1 1 … 1 ) ) .
摘要翻译：用于估计磁共振（MR）图像的线圈灵敏度映射的方法包括提供（61）线圈校准数据的2D图像的滑块的矩阵A，从奇异值计算（62）左奇异矩阵V∥ 计算（63）P = V || V | H，计算（64）作为零填充矩阵P的逆傅里叶变换的矩阵S，求解（65）MHcr = （Sr）Hcr，其中cr是空间位置r处所有线圈的线圈灵敏度图的向量，M（（1 1 ... 1 0 0 ... 0 ... ... ... 0 0 ... 0）0（0 0 ... 0 1 1 ... 1 ... ... ... 0 0 ... 0） 0 0 0 ... 0 ... ... ... 1 1 ... 1））。

7. 发明申请

US20140086469A1 MRI RECONSTRUCTION WITH INCOHERENT SAMPLING AND REDUNDANT HAAR WAVELETS 有权
标题翻译： MRI重建采用不寻常的采样和冗余风险
公开(公告)号：US20140086469A1
公开(公告)日：2014-03-27
申请号：US14036352
申请日：2013-09-25
申请人： Alban Lefebvre , Jun Liu , Edgar Mueller , Mariappan S. Nadar , Michaela Schmidt , Michael Zenge , Qiu Wang
发明人： Alban Lefebvre , Jun Liu , Edgar Mueller , Mariappan S. Nadar , Michaela Schmidt , Michael Zenge , Qiu Wang
IPC分类号： G06T11/00
CPC分类号： G06T11/005 , G01R33/482 , G01R33/4824 , G01R33/5608 , G01R33/5611 , G01R33/56308 , G01R33/5635
摘要： A method of image reconstruction for a magnetic resonance imaging (MRI) system having a plurality of coils includes obtaining k-space scan data captured by the MRI system, the k-space scan data being representative of an undersampled region over time, determining a respective coil sensitivity profile for the region for each coil of the plurality of coils, and iteratively reconstructing dynamic images for the region from the k-space scan data via an optimization of a minimization problem. The minimization problem is based on the determined coil sensitivity profiles and redundant Haar wavelet transforms of the dynamic images.
摘要翻译：一种用于具有多个线圈的磁共振成像（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空间扫描数据表示随时间推移的欠采样区域，确定相应的线圈灵敏度分布，用于多个线圈的每个线圈的区域，并且通过最小化问题的优化从k空间扫描数据迭代地重建该区域的动态图像。最小化问题是基于确定的线圈灵敏度分布和动态图像的冗余哈尔小波变换。

8. 发明申请

US20140085318A1 Multi-GPU FISTA Implementation for MR Reconstruction with Non-Uniform K-Space Sampling 有权
标题翻译：用于非均匀K空间采样的MR重建的多GPU FISTA实现
公开(公告)号：US20140085318A1
公开(公告)日：2014-03-27
申请号：US14031374
申请日：2013-09-19
申请人： Mariappan S. Nadar , Steven Martin , Alban Lefebvre , Jun Liu
发明人： Mariappan S. Nadar , Steven Martin , Alban Lefebvre , Jun Liu
IPC分类号： G06T7/00
CPC分类号： G06T7/0012 , G01R33/4824 , G01R33/5608
摘要： A system for performing image reconstruction in a multi-threaded computing environment includes one or more central processing units executing a plurality of k-space components and a plurality of graphic processing units executing a reconstruction component. The k-space components executing on the central processing units include a k-space sample data component operating in a first thread and configured to receive k-space sample data from a first file interface; a k-space sample coordinate data component operating in a second thread and configured to receive k-space sample coordinate data from a second file interface; and a k-space sample weight data component operating in a third thread and configured to retrieve k-space sample weight data from a third file interface. The reconstruction component is configured to receive one or more k-space input data buffers comprising the k-space sample data, the k-space sample coordinate data, and the k-space sample weight data from the one or more central processing units, and reconstruct an image based on the input data buffers using an iterative reconstruction algorithm.
摘要翻译：用于在多线程计算环境中执行图像重建的系统包括执行多个k空间分量的一个或多个中央处理单元和执行重构分量的多个图形处理单元。在中央处理单元上执行的k空间分量包括在第一线程中操作并被配置为从第一文件接口接收k空间样本数据的k空间样本数据分量; 操作在第二线程中并被配置为从第二文件接口接收k空间样本坐标数据的k空间样本坐标数据分量; 以及在第三线程中操作并被配置为从第三文件接口检索k空间样本权重数据的k空间样本权重数据分量。重构分量被配置为从一个或多个中央处理单元接收一个或多个k空间输入数据缓冲器，其包括k空间样本数据，k空间样本坐标数据和k空间样本权重数据，以及使用迭代重建算法基于输入数据缓冲器重构图像。

9. 发明申请

US20140037228A1 ZERO COMMUNICATION BLOCK PARTITIONING 审中-公开
标题翻译：零通讯块分割
公开(公告)号：US20140037228A1
公开(公告)日：2014-02-06
申请号：US13950535
申请日：2013-07-25
申请人： Alban Lefebvre , Axel Loewe , Mariappan S. Nadar , Jun Liu
发明人： Alban Lefebvre , Axel Loewe , Mariappan S. Nadar , Jun Liu
IPC分类号： G06T1/20
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。

10. 发明授权

US09519981B2 Visualizing brain network connectivity 有权
标题翻译：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。

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