Physiological Signal Reconstruction using Deep Learning

Description

Functional magnetic resonance imaging (fMRI) is a powerful technique for studying human brain activity and large-scale neural circuits. However, fMRI signals can be strongly modulated by slow changes in respiration volume (RV) and heart rate (HR). Monitoring cardiac and respiratory signals during fMRI enables modeling and/or reducing such effects; yet, physiological measurements are often unavailable in practice, and are missing from a large number of fMRI datasets. Here, we propose learning approaches for inferring RV and HR signals directly from fMRI time-series dynamics.

Publications

"A Deep Pattern Recognition Approach for Inferring Respiratory Volume Fluctuations from fMRI Data" accepted to MICCAI (2020).

"Reconstruction of respiratory variation signals from fMRI data" accepted to Neuroimage (2020).

"From Brain to Body: Learning Low-Frequency Respiration and Cardiac Signals from fMRI Dynamics" accepted to MICCAI (2021).