Centre d'Imagerie Biomédicale, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Laboratoire for Functional and Metabolic Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Magn Reson Med. 2017 Sep;78(3):888-896. doi: 10.1002/mrm.26823. Epub 2017 Jul 7.
Physiological noise often dominates the blood-oxygen level-dependent (BOLD) signal fluctuations in high-field functional MRI (fMRI) data. Therefore, to optimize fMRI protocols, it becomes crucial to investigate how physiological signal fluctuations impact various acquisition and reconstruction schemes at different acquisition speeds. In particular, further differences can arise between 2D and 3D fMRI acquisitions due to different encoding strategies, thereby impacting fMRI sensitivity in potentially significant ways.
The amount of physiological noise to be removed from the BOLD fMRI signal acquired at 7 T was quantified for different sampling rates (repetition time from 3300 to 350 ms, acceleration 1 to 8) and techniques dedicated to fast fMRI (simultaneous multislice echo planar imaging [EPI] and 3D EPI). Resting state fMRI (rsfMRI) performances were evaluated using temporal signal-to-noise ratio (tSNR) and network characterization based on seed correlation and independent component analysis.
Overall, acceleration enhanced tSNR and rsfMRI metrics. 3D EPI benefited the most from physiological noise removal at long repetition times. Differences between 2D and 3D encoding strategies disappeared at high acceleration factors (6- to 8-fold).
After physiological noise correction, 2D- and 3D-accelerated sequences provide similar performances at high fields, both in terms of tSNR and resting state network identification and characterization. Magn Reson Med 78:888-896, 2017. © 2017 International Society for Magnetic Resonance in Medicine.
生理噪声通常主导高磁场功能磁共振成像(fMRI)数据中血氧水平依赖(BOLD)信号的波动。因此,为了优化 fMRI 方案,研究生理信号波动如何影响不同采集速度下的各种采集和重建方案变得至关重要。特别是,由于不同的编码策略,2D 和 3D fMRI 采集之间会出现进一步的差异,从而以潜在显著的方式影响 fMRI 的灵敏度。
针对不同的采样率(重复时间从 3300 到 350 毫秒,加速因子为 1 到 8)和专门用于快速 fMRI 的技术(同时多切片回波平面成像[EPI]和 3D EPI),对 7T 采集的 BOLD fMRI 信号中要去除的生理噪声量进行了量化。使用时频信号噪声比(tSNR)和基于种子相关和独立成分分析的网络特征评估静息态 fMRI(rsfMRI)性能。
总体而言,加速提高了 tSNR 和 rsfMRI 指标。3D EPI 在长重复时间下受益于生理噪声去除的程度最大。在高加速因子(6 到 8 倍)下,2D 和 3D 编码策略之间的差异消失。
在进行生理噪声校正后,2D 和 3D 加速序列在高磁场下提供了类似的性能,无论是在 tSNR 还是在静息态网络识别和特征方面。磁共振医学 78:888-896, 2017。© 2017 国际磁共振学会。