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

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