Motivation: In many fMRI studies, respiratory signals are often missing or of
poor quality. Therefore, it could be highly beneficial to have a tool to
extract respiratory variation (RV) waveforms directly from fMRI data without
the need for peripheral recording devices.
Goal(s): Investigate the hypothesis that head motion parameters contain
valuable information regarding respiratory patter, which can help machine
learning algorithms estimate the RV waveform.
Approach: This study proposes a CNN model for reconstruction of RV waveforms
using head motion parameters and BOLD signals.
Results: This study showed that combining head motion parameters with BOLD
signals enhances RV waveform estimation.
Impact: It is expected that application of the proposed method will lower the
cost of fMRI studies, reduce complexity, and decrease the burden on
participants as they will not be required to wear a respiratory bellows.

该研究提出了一种使用头部运动参数和 BOLD 信号来重构 RV 波形的 CNN 模型，结果表明将头部运动参数与 BOLD 信号相结合可以增强 RV 波形估计。

基于机器学习的 BOLD fMRI 和头动参数对健康成年人呼吸波动的估计

Machine Learning-based Estimation of Respiratory Fluctuations in a  Healthy Adult Population using BOLD fMRI and Head Motion Parameters

In many fMRI studies, respiratory signals are unavailable or do not have
acceptable quality. Consequently, the direct removal of low-frequency
respiratory variations from BOLD signals is not possible. This study proposes a
one-dimensional CNN model for reconstruction of two respiratory measures, RV
and RVT. Results show that a CNN can capture informative features from resting
BOLD signals and reconstruct realistic RV and RVT timeseries. It is expected
that application of the proposed method will lower the cost of fMRI studies,
reduce complexity, and decrease the burden on participants as they will not be
required to wear a respiratory bellows.

本研究提出了一种一维 CNN 模型用于重建两个呼吸参数，RV 和 RVT，结果表明 CNN 可以从静息状态的 BOLD 信号中捕获有用的特征并重建真实的 RV 和 RVT 时间序列，预期这种方法的应用将降低 fMRI 研究的成本、减少复杂性并减轻参与者的负担，因为他们不需要佩戴呼吸器。

使用卷积神经网络 (CNN) 结合 BOLD-fMRI 计算人体连接组发育队列中的单位时间呼吸体积 (RTV) 和呼吸变异度 (RV)

Using BOLD-fMRI to Compute the Respiration Volume per Time (RTV) and  Respiration Variation (RV) with Convolutional Neural Networks (CNN) in the  Human Connectome Development Cohort

Tracking organ motion is important in image-guided interventions, but motion
annotations are not always easily available. Thus, we propose Repetitive Motion
Estimation Network (RMEN) to recover cardiac and respiratory signals. It learns
the spatio-temporal repetition patterns, embedding high dimensional motion
manifolds to 1D vectors with partial motion phase boundary annotations.
Compared with the best alternative models, our proposed RMEN significantly
decreased the QRS peaks detection offsets by 59.3%. Results showed that RMEN
could handle the irregular cardiac and respiratory motion cases. Repetitive
motion patterns learned by RMEN were visualized and indicated in the feature
maps.

提出 Repetitive Motion Estimation Network（RMEN）用于恢复心脏和呼吸信号，通过学习时空重复模式将高维运动流形嵌入带有部分运动相位边界注释的一维向量。结果表明，RMEN 可以处理不规则的心脏和呼吸运动，并且比最佳替代模型显著减少 QRS 峰检测偏移量。