Fang Zhongnan, Van Le Nguyen, Choy ManKin, Lee Jin Hyung
Department of Electrical Engineering, Stanford University, Stanford, California, USA.
Department of Electrical Engineering, University of California, Los Angeles, Los Angeles, California, USA.
Magn Reson Med. 2016 Aug;76(2):440-55. doi: 10.1002/mrm.25854. Epub 2015 Oct 29.
To propose a novel compressed sensing (CS) high spatial resolution functional MRI (fMRI) method and demonstrate the advantages and limitations of using CS for high spatial resolution fMRI.
A randomly undersampled variable density spiral trajectory enabling an acceleration factor of 5.3 was designed with a balanced steady state free precession sequence to achieve high spatial resolution data acquisition. A modified k-t SPARSE method was then implemented and applied with a strategy to optimize regularization parameters for consistent, high quality CS reconstruction.
The proposed method improves spatial resolution by six-fold with 12 to 47% contrast-to-noise ratio (CNR), 33 to 117% F-value improvement and maintains the same temporal resolution. It also achieves high sensitivity of 69 to 99% compared the original ground-truth, small false positive rate of less than 0.05 and low hemodynamic response function distortion across a wide range of CNRs. The proposed method is robust to physiological noise and enables detection of layer-specific activities in vivo, which cannot be resolved using the highest spatial resolution Nyquist acquisition.
The proposed method enables high spatial resolution fMRI that can resolve layer-specific brain activity and demonstrates the significant improvement that CS can bring to high spatial resolution fMRI. Magn Reson Med 76:440-455, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
提出一种新型的压缩感知（CS）高空间分辨率功能磁共振成像（fMRI）方法，并论证将CS用于高空间分辨率fMRI的优势与局限性。
设计了一种随机欠采样可变密度螺旋轨迹，结合平衡稳态自由进动序列实现5.3倍的加速因子，以完成高空间分辨率的数据采集。随后实施一种改进的k-t SPARSE方法，并采用一种策略优化正则化参数，以实现一致、高质量的CS重建。
所提出的方法将空间分辨率提高了6倍，对比噪声比（CNR）提高了12%至47%，F值提高了33%至117%，并保持了相同的时间分辨率。与原始真实数据相比，其还具有69%至99%的高灵敏度、小于0.05的低假阳性率以及在广泛的CNR范围内的低血液动力学响应函数失真。所提出的方法对生理噪声具有鲁棒性，能够在体内检测层特异性活动，而这是使用最高空间分辨率奈奎斯特采集无法分辨的。
所提出的方法能够实现可分辨层特异性脑活动的高空间分辨率fMRI，并论证了CS可给高空间分辨率fMRI带来的显著改善。《磁共振医学》76:440 - 455, 2016。© 2015作者。《磁共振医学》由威利期刊公司代表国际磁共振医学学会出版。这是一篇根据知识共享署名 - 非商业性使用 - 禁止演绎许可条款的开放获取文章，允许在任何媒介中使用和传播，前提是正确引用原始作品，使用是非商业性的，且不进行任何修改或改编。
