Abstract #2709

Using BOLD-fMRI to Compute Respiration Volume per Time (RVT) and Respiration Variation (RV) with Convolutional Neural Network (CNN) in Children

Abdoljalil Addeh^1,2, Fernando Vega^1,2, Rebecca J. Williams^2,3,4, Ali Golestani⁵, G. Bruce Pike^2,3,4, and M. Ethan MacDonald ^1,2,3,6

¹Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada, ²Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada, ³Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada, ⁴Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada, ⁵Department of Medical Physics, Alberta Heath Services, Calgary, AB, Canada, ⁶Department of Electrical & Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada

Synopsis

Keywords: Data Processing, fMRI, Physiological NoiseIn 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 including RV and RVT. Results show that a CNN can capture informative features from the BOLD signals and reconstruct accurate 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 because they will not be required to wear a respiratory bellows

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