(ISMRM 2024) Machine Learning-based Estimation of Respiratory Fluctuations in a Healthy Adult Population using BOLD fMRI and Head Motion Parameters
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Abstract #3276

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

Abdoljalil Addeh1,2,3,4, Fernando Vega1,2,3,4, Rebecca J. Williams5, G. Bruce Pike2,6,7, 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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Keywords

head