Chang Hing-Chiu, Gaur Pooja, Chou Ying-hui, Chu Mei-Lan, Chen Nan-kuei
Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, United States of America.
Department of Chemical and Physical Biology, Vanderbilt University, Nashville, TN, United States of America; Vanderbilt University Institute of Imaging Science, Nashville, TN, United States of America.
PLoS One. 2014 Dec 30;9(12):e116378. doi: 10.1371/journal.pone.0116378. eCollection 2014.
Functional magnetic resonance imaging (fMRI) is a non-invasive and powerful imaging tool for detecting brain activities. The majority of fMRI studies are performed with single-shot echo-planar imaging (EPI) due to its high temporal resolution. Recent studies have demonstrated that, by increasing the spatial-resolution of fMRI, previously unidentified neuronal networks can be measured. However, it is challenging to improve the spatial resolution of conventional single-shot EPI based fMRI. Although multi-shot interleaved EPI is superior to single-shot EPI in terms of the improved spatial-resolution, reduced geometric distortions, and sharper point spread function (PSF), interleaved EPI based fMRI has two main limitations: 1) the imaging throughput is lower in interleaved EPI; 2) the magnitude and phase signal variations among EPI segments (due to physiological noise, subject motion, and B0 drift) are translated to significant in-plane aliasing artifact across the field of view (FOV). Here we report a method that integrates multiple approaches to address the technical limitations of interleaved EPI-based fMRI. Firstly, the multiplexed sensitivity-encoding (MUSE) post-processing algorithm is used to suppress in-plane aliasing artifacts resulting from time-domain signal instabilities during dynamic scans. Secondly, a simultaneous multi-band interleaved EPI pulse sequence, with a controlled aliasing scheme incorporated, is implemented to increase the imaging throughput. Thirdly, the MUSE algorithm is then generalized to accommodate fMRI data obtained with our multi-band interleaved EPI pulse sequence, suppressing both in-plane and through-plane aliasing artifacts. The blood-oxygenation-level-dependent (BOLD) signal detectability and the scan throughput can be significantly improved for interleaved EPI-based fMRI. Our human fMRI data obtained from 3 Tesla systems demonstrate the effectiveness of the developed methods. It is expected that future fMRI studies requiring high spatial-resolvability and fidelity will largely benefit from the reported techniques.
功能磁共振成像（fMRI）是一种用于检测大脑活动的非侵入性且强大的成像工具。由于其高时间分辨率，大多数fMRI研究都是采用单次激发回波平面成像（EPI）进行的。最近的研究表明，通过提高fMRI的空间分辨率，可以测量以前未识别的神经网络。然而，提高基于传统单次激发EPI的fMRI的空间分辨率具有挑战性。尽管多激发交错EPI在提高空间分辨率、减少几何失真和更清晰的点扩散函数（PSF）方面优于单次激发EPI，但基于交错EPI的fMRI有两个主要局限性：1）交错EPI的成像通量较低；2）EPI段之间的幅度和相位信号变化（由于生理噪声、受试者运动和B0漂移）会转化为整个视野（FOV）内显著的平面内混叠伪影。在此，我们报告一种整合多种方法来解决基于交错EPI的fMRI技术局限性的方法。首先，使用多路复用灵敏度编码（MUSE）后处理算法来抑制动态扫描期间时域信号不稳定性导致的平面内混叠伪影。其次，实施一种结合了可控混叠方案的同时多频带交错EPI脉冲序列，以提高成像通量。第三，然后将MUSE算法进行推广，以适应使用我们的多频带交错EPI脉冲序列获得的fMRI数据，抑制平面内和平面间的混叠伪影。对于基于交错EPI的fMRI，血氧水平依赖（BOLD）信号的可检测性和扫描通量可以得到显著提高。我们从3特斯拉系统获得的人体fMRI数据证明了所开发方法的有效性。预计未来需要高空间分辨率和保真度的fMRI研究将从所报道的技术中大大受益。
