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
Abdoljalil Addeh Fernando Vega Rebecca J Williams Ali Golestani G. Bruce Pike M. Ethan MacDonald
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.