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1. 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））。

2. 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））。

3. 发明授权

US09097780B2 Multi-stage magnetic resonance reconstruction for parallel imaging applications 有权
标题翻译：并行成像应用的多级磁共振重建
公开(公告)号：US09097780B2
公开(公告)日：2015-08-04
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： G06K9/00 , G01R33/561
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。

4. 发明申请

US20140133724A1 MULTI-STAGE MAGNETIC RESONANCE RECONSTRUCTION FOR PARALLEL IMAGING APPLICATIONS 有权
标题翻译：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。

5. 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。

6. 发明授权

US09482732B2 MRI reconstruction with motion-dependent regularization 有权
标题翻译： 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。

7. 发明申请

US20140126796A1 MRI RECONSTRUCTION WITH MOTION-DEPENDENT REGULARIZATION 有权
标题翻译： MRI重建与运动依赖性定期
公开(公告)号：US20140126796A1
公开(公告)日：2014-05-08
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： G06T11/00 , G06T7/20
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。

8. 发明授权

US09453895B2 Dynamic image reconstruction with tight frame learning 有权
标题翻译：动态图像重建与紧帧学习
公开(公告)号：US09453895B2
公开(公告)日：2016-09-27
申请号：US14027451
申请日：2013-09-16
申请人： Jun Liu , Qiu Wang , Mariappan Nadar , Michael Zenge , Edgar Mueller
发明人： Jun Liu , Qiu Wang , Mariappan Nadar , Michael Zenge , Edgar Mueller
IPC分类号： G01R33/48 , G06T5/50 , G01R33/561
CPC分类号： G01R33/4818 , G01R33/5611 , G06T5/50
摘要： A computer-implemented method for learning a tight frame includes acquiring undersampled k-space data over a time period using an interleaved process. An average of the undersampled k-space data is determined and a reference image is generated based on the average of the undersampled k-space data. Next, a tight frame operator is determined based on the reference image. Then, a reconstructed image data is generated from the undersampled k-space data via a sparse reconstruction which utilizes the tight frame operator.
摘要翻译：用于学习紧帧的计算机实现的方法包括使用交错处理在一段时间内获取欠采样的k空间数据。确定欠采样的k空间数据的平均值，并且基于欠采样的k空间数据的平均值生成参考图像。接下来，基于参考图像确定紧帧运算符。然后，通过使用紧帧运算符的稀疏重构，从欠采样的k空间数据生成重建的图像数据。

9. 发明申请

US20140097845A1 Dynamic Image Reconstruction with Tight Frame Learning 有权
标题翻译：动态图像重建与紧帧学习
公开(公告)号：US20140097845A1
公开(公告)日：2014-04-10
申请号：US14027451
申请日：2013-09-16
申请人： Jun Liu , Qiu Wang , Mariappan Nadar , Michael Zenge , Edgar Mueller
发明人： Jun Liu , Qiu Wang , Mariappan Nadar , Michael Zenge , Edgar Mueller
IPC分类号： G01R33/48 , G06T5/50
CPC分类号： G01R33/4818 , G01R33/5611 , G06T5/50
摘要： A computer-implemented method for learning a tight frame includes acquiring undersampled k-space data over a time period using an interleaved process. An average of the undersampled k-space data is determined and a reference image is generated based on the average of the undersampled k-space data. Next, a tight frame operator is determined based on the reference image. Then, a reconstructed image data is generated from the undersampled k-space data via a sparse reconstruction which utilizes the tight frame operator.
摘要翻译：用于学习紧帧的计算机实现的方法包括使用交错处理在一段时间内获取欠采样的k空间数据。确定欠采样的k空间数据的平均值，并且基于欠采样的k空间数据的平均值生成参考图像。接下来，基于参考图像确定紧帧运算符。然后，通过使用紧帧运算符的稀疏重构，从欠采样的k空间数据生成重建的图像数据。

10. 发明申请

US20160247299A1 Generalized Approximate Message Passing Algorithms for Sparse Magnetic Resonance Imaging Reconstruction 有权
标题翻译：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。

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