Chuang K H, Chen J H
Interdisciplinary MRI/MRS Laboratory, Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan, R.O.C.
Magn Reson Med. 2001 Aug;46(2):344-53. doi: 10.1002/mrm.1197.
Functional MRI (fMRI) signal variation induced by respiratory and cardiac motion affects the activation signal and limits the accuracy of analysis. Current physiological motion correction methods require either synchronization with external monitoring of respiration and heartbeat, specialized pulse sequence design, or k-space data. The IMage-based Physiological Artifacts estimation and Correction Technique (IMPACT), which is free from these constraints, is described. When images are acquired fast enough to sample physiological motion without aliasing, respiratory and cardiac signals can be directly estimated from magnitude images. Physiological artifacts are removed by reordering images according to the estimated respiratory and cardiac phases and then subtracting the Fourier-fitted variation from magnitude images. Compared with the k-space-based method, this method can efficiently and effectively reduce the overall signal fluctuation in the brain and increase the activated area. With this new technique, physiological artifacts can be reduced using traditional fMRI pulse sequences, and existing data can be corrected and reanalyzed without additional experiments.
呼吸和心脏运动引起的功能磁共振成像(fMRI)信号变化会影响激活信号并限制分析的准确性。当前的生理运动校正方法要么需要与呼吸和心跳的外部监测同步,要么需要专门的脉冲序列设计,要么需要k空间数据。本文介绍了基于图像的生理伪影估计与校正技术(IMPACT),该技术不受这些限制。当图像采集速度足够快以对生理运动进行采样而不产生混叠时,可以直接从幅度图像中估计呼吸和心脏信号。通过根据估计的呼吸和心脏相位对图像进行重新排序,然后从幅度图像中减去傅里叶拟合的变化来去除生理伪影。与基于k空间的方法相比,该方法可以有效且高效地减少大脑中的整体信号波动并增加激活区域。使用这项新技术,可以使用传统的fMRI脉冲序列减少生理伪影,并且无需额外实验即可对现有数据进行校正和重新分析。