Abstract #3276

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

Abdoljalil Addeh^1,2,3,4, Fernando Vega^1,2,3,4, Rebecca J. Williams⁵, G. Bruce Pike^2,6,7, and M. Ethan MacDonald ^1,2,3,4

¹Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB, Canada, ²Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada, ³Department of Electrical & Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada, ⁴Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada, ⁵Faculty of Health, Charles Darwin University, Australia, Darwin, Australia, ⁶Department of Radiology, University of Calgary, Calgary, AB, Canada, Calgary, AB, Canada, ⁷Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada

Synopsis

Keywords: fMRI Analysis, fMRI

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.

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