Time‐domain principal component reconstruction (tPCR): A more efficient and stable iterative reconstruction framework for non‐Cartesian functional MRI
Time‐domain principal component reconstruction (tPCR): A more efficient and stable iterative reconstruction framework for non‐Cartesian functional MRI
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时域主成分重建 (tPCR)：一种更高效、更稳定的非笛卡尔功能 MRI 迭代重建框架
DOI：
10.1002/mrm.28208
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发表时间：
2020
影响因子：
3.3
通讯作者：
LeVan P
中科院分区：
文献类型：
--
作者：
Wang F;Hennig J;LeVan P
目的为了提高非笛卡尔函数磁共振成像迭代重建的效率(即计算量)和稳定性，在高欠采样率和/或存在强非共振效应的情况下，理论和方法采用具有3D非笛卡尔轨迹和0.1s重复时间的磁共振脑成像(MREG)序列来获取fMRI数据集。与传统的逐时逐点序列重建不同，本文提出的时间域主成分重建方法包括三个步骤：(1)利用奇异值分解将k-t-空间fMRI数据集分解到时间域主成分空间；(2)根据每个主成分的权重重新分配计算能力；(3)将重构后的主成分组合回图像-t-空间。结果仿真实验表明，相对于SR，在相同的计算代价下，该方法能够显著减少重建误差和后续的功能激活误差。或者，在固定重建精度的情况下，计算时间大大减少。与L2范数线性重构相比，L1范数非线性重构的性能改善尤为明显，并且对不同的正则化强度、欠采样率和非共振效应强度具有较强的稳健性。通过对激活图的检验，TPCR在真实的fMRI实验中也有类似的改善。结论所提出的概念验证TPCR框架可以提高(1)迭代重建的重建效率，(2)重建的稳定性，特别是对于非线性重建。作为一个实用的考虑，改进的重建速度促进了高度欠采样的非笛卡尔快速fMRI的应用。
PurposeTo improve the reconstruction efficiency (i.e., computational load) and stability of iterative reconstruction for non‐Cartesian fMRI when using high undersampling rates and/or in the presence of strong off‐resonance effects.Theory and MethodsThe magnetic resonance encephalography (MREG) sequence with 3D non‐Cartesian trajectory and 0.1s repetition time (TR) was applied to acquire fMRI datasets. Different from a conventional time‐point‐by‐time‐point sequential reconstruction (SR), the proposed time‐domain principal component reconstruction (tPCR) performs three steps: (1) decomposing the k‐t‐space fMRI datasets into time‐domain principal component space using singular value decomposition, (2) reconstructing each principal component with redistributed computation power according to their weights, and (3) combining the reconstructed principal components back to image‐t‐space. The comparison of reconstruction accuracy was performed by simulation experiments and then verified in real fMRI data.ResultsThe simulation experiments showed that the proposed tPCR was able to significantly reduce reconstruction errors, and subsequent functional activation errors, relative to SR at identical computational cost. Alternatively, at fixed reconstruction accuracy, computation time was greatly reduced. The improved performance was particularly obvious for L1‐norm nonlinear reconstructions relative to L2‐norm linear reconstructions and robust to different regularization strength, undersampling rates, and off‐resonance effects intensity. By examining activation maps, tPCR was also found to give similar improvements in real fMRI experiments.ConclusionThe proposed proof‐of‐concept tPCR framework could improve (1) the reconstruction efficiency of iterative reconstruction, and (2) the reconstruction stability especially for nonlinear reconstructions. As a practical consideration, the improved reconstruction speed promotes the application of highly undersampled non‐Cartesian fast fMRI.
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影响因子：
3.3
作者：
Uecker, Martin;Lai, Peng;Murphy, Mark J.;Virtue, Patrick;Elad, Michael;Pauly, John M.;Vasanawala, Shreyas S.;Lustig, Michael
通讯作者：
Lustig, Michael
影响因子：
5.7
作者：
Posse, Stefan;Ackley, Elena;Mutihac, Radu;Rick, Jochen;Shane, Matthew;Murray-Krezan, Cristina;Zaitsev, Maxim;Speck, Oliver
通讯作者：
Speck, Oliver
影响因子：
4.8
作者：
Akin, Burak;Lee, Hsu-Lei;LeVan, Pierre
通讯作者：
LeVan, Pierre
影响因子：
3.8
作者：
Wang, Haoyu;Miao, Yanwei;Hu, Jiani
通讯作者：
Hu, Jiani
在临床功能 MR 成像中使用简单的屏气任务进行脑血管反应性扫描
DOI：
--
发表时间：
2016
期刊：
影响因子：
--
作者：
J. Peacock;D. Black;D. DeLone;K. Welker
通讯作者：
K. Welker