Wang Fei, Hennig Jürgen, LeVan Pierre
Department of Radiology, Medical Physics, Faculty of Medicine, Medical Center - University of Freiburg, Freiburg, Germany.
Center for Basics in NeuroModulation (NeuroModul Basics), Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Magn Reson Med. 2020 Sep;84(3):1321-1335. doi: 10.1002/mrm.28208. Epub 2020 Feb 18.
To 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.
The 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.
The 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.
The 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.
在使用高欠采样率和/或存在强失谐效应的情况下，提高非笛卡尔功能磁共振成像（fMRI）迭代重建的效率（即计算负荷）和稳定性。
采用具有三维非笛卡尔轨迹和0.1秒重复时间（TR）的磁共振脑造影（MREG）序列来采集fMRI数据集。与传统的逐时间点顺序重建（SR）不同，所提出的时域主成分重建（tPCR）执行三个步骤：（1）使用奇异值分解将k-t空间fMRI数据集分解到时域主成分空间，（2）根据权重重新分配计算能力来重建每个主成分，以及（3）将重建后的主成分组合回图像t空间。通过模拟实验进行重建精度的比较，然后在实际fMRI数据中进行验证。
模拟实验表明，在所提出的tPCR在相同计算成本下能够显著降低重建误差以及后续的功能激活误差。或者，在固定重建精度下，计算时间大幅减少。相对于L2范数线性重建，L1范数非线性重建的性能提升尤为明显，并且对不同的正则化强度、欠采样率和失谐效应强度具有鲁棒性。通过检查激活图，还发现在实际fMRI实验中tPCR也有类似的改进。
所提出的概念验证tPCR框架可以提高（1）迭代重建的效率，以及（2）重建稳定性，特别是对于非线性重建。从实际考虑，提高的重建速度促进了高欠采样非笛卡尔快速fMRI的应用。
