WHOCARES：从高度加速的同时多层 fMRI 采集中进行数据驱动的全脑心脏信号回归
WHOCARES: data-driven WHOle-brain CArdiac signal REgression from highly accelerated simultaneous multi-Slice fMRI acquisitions
AI总结：
针对fMRI中心脏搏动信号混叠问题，提出数据驱动的全脑心脏信号回归方法WHOCARES，结合超采样分解与SMS技术，在无外部生理记录下实现逐体素心脏波形解析与fMRI数据回顾性校正。
摘要：
心脏搏动是功能磁共振成像（fMRI）时间序列的一种生理混淆因素，它会在血管附近引入虚假的信号波动。仅靠 fMRI 本身速度不足以解析心脏搏动。取决于瞬时心率与采集采样频率（1/TR，TR 为重复时间）之间的比率，心脏信号可能会混叠到神经激活的频带中。同时多层（SMS）成像的引入显著降低了心脏混叠的几率。然而，在高空间分辨率下覆盖全脑的必要性将最短 TR 限制在略高于 0.5 秒，这反过来又不足以解析超过每分钟 60 次的心脏搏动。最近，fMRI 时间序列的超采样被引入以克服这一问题。虽然每个解剖位置每 TR 秒被采样一次，但连续激发之间的时间更短，因此足以解析心脏搏动。在本研究中，我们展示了通过将专用的超采样分解方案与 SMS 相结合，在时间和空间上解析每个体素位置的心脏波形是可行的。我们在从 Human Connectome Project (HCP) 中选取的 774 名健康受试者上开发了该技术，并针对 RETROICOR 方法对该技术进行了验证。我们提出的这种方法命名为从高度加速的同时多层 fMRI 采集中进行数据驱动的全脑心脏信号回归（WHOCARES），它是完全数据驱动的，不对心脏搏动性做特定假设，并且独立于外部生理记录，从而使得在此类测量不可用时对 fMRI 数据进行回顾性校正成为可能。WHOCARES 可在 https://github.com/gferrazzi/WHOCARES 免费获取。
英文摘要：
Cardiac pulsation is a physiological confound of functional magnetic resonance imaging (fMRI) time-series that introduces spurious signal fluctuations in proximity to blood vessels. fMRI alone is not sufficiently fast to resolve cardiac pulsation. Depending on the ratio between the instantaneous heart-rate and the acquisition sampling frequency (1/TR, with TR being the repetition time), the cardiac signal may alias into the frequency band of neural activation. The introduction of simultaneous multi-slice (SMS) imaging has significantly reduced the chances of cardiac aliasing. However, the necessity of covering the entire brain at high spatial resolution restrain the shortest TR to just over 0.5 seconds, which is in turn not sufficiently short to resolve cardiac pulsation beyond 60 beats per minute. Recently, hyper-sampling of the fMRI time-series has been introduced to overcome this issue. While each anatomical location is sampled every TR seconds, the time between consecutive excitations is shorter and thus adequate to resolve cardiac pulsation. In this study, we show that it is feasible to temporally and spatially resolve cardiac waveforms at each voxel location by combining a dedicated hyper-sampling decomposition scheme with SMS. We developed the technique on 774 healthy subjects selected from the Human Connectome Project (HCP) and validated the technology against the RETROICOR method. The proposed approach, which we name Data-driven WHOle-brain CArdiac signal REgression from highly accelerated simultaneous multi-Slice fMRI acquisitions (WHOCARES), is fully data-driven, does not make specific assumptions on cardiac pulsatility, and is independent from external physiological recordings so that the retrospective correction of fMRI data becomes possible when such measurements are not available. WHOCARES is freely available at https://github.com/gferrazzi/WHOCARES.
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