用头动参数提升脑部扫描呼吸干扰的估计精度
The Useful Side of Motion: Using Head Motion Parameters to Correct for Respiratory Confounds in BOLD fMRI
- 用一维卷积网络融合头动数据估计呼吸变化
- 带通滤波后的头动参数能显著提高估计准确率
- 适合做功能磁共振数据预处理的研究者
在功能磁共振成像（fMRI）中获取准确的外部呼吸数据具有挑战性，因此研究者探索利用机器学习从fMRI数据中估计呼吸变异（RV）。呼吸会引起头动，包括真实和伪头动，这些信息可能包含呼吸事件的有用信号。建议的陷波滤波器可减轻呼吸引起的运动伪影，表明在呼吸频率带内使用带通滤波器可分离出呼吸诱导的头动。本研究旨在通过整合估计的头动参数来提升静息态BOLD-fMRI数据中呼吸变异的估计精度，重点考察原始与带通滤波后头动参数对1D-CNN模型重构准确性的影响。该方法克服了传统滤波技术的局限性，充分利用头动数据以实现更稳健的呼吸相关变化估计。
原文摘要 · Abstract (English)
Acquiring accurate external respiratory data during functional Magnetic Resonance Imaging (fMRI) is challenging, prompting the exploration of machine learning methods to estimate respiratory variation (RV) from fMRI data. Respiration induces head motion, including real and pseudo motion, which likely provides useful information about respiratory events. Recommended notch filters mitigate respiratory-induced motion artifacts, suggesting that a bandpass filter at the respiratory frequency band isolates respiratory-induced head motion. This study seeks to enhance the accuracy of RV estimation from resting-state BOLD-fMRI data by integrating estimated head motion parameters. Specifically, we aim to determine the impact of incorporating raw versus bandpass-filtered head motion parameters on RV reconstruction accuracy using one-dimensional convolutional neural networks (1D-CNNs). This approach addresses the limitations of traditional filtering techniques and leverages the potential of head motion data to provide a more robust estimation of respiratory-induced variations.
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