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Abstract #2607

Isolating cardiac-related pulsatility in blood oxygenation level-dependent MRI with deep learning

Jake Valsamis1, Nicholas Luciw1, Nandinee Haq1, Sarah Atwi 1, Simon Duchesne 2,3, William Cameron1, and Bradley J MacIntosh1,4
1Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada, 2Radiology Department, Faculty of Medicine, Laval University, Québec, QC, Canada, 3Quebec City Mental Health Institute, Québec, QC, Canada, 4Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada


Persistent exposure to highly pulsatile blood can damage the brain’s microvasculature. A convenient method for measuring cerebral pulsatility would allow investigation into its relationship with vascular dysfunction and cognitive decline. In this work, we propose a convolutional neural network (CNN) based deep learning solution to estimate cerebral pulsatility using only the frequency content from BOLD MRI scans. Various frequency component inputs were assessed, and echo time dependence was evaluated with a 5-fold cross-validation. Pulsatility was estimated from BOLD MRI data acquired on a different scanner to assess generalizability. The CNN reliably estimated pulsatility and was robust to various scan parameters.

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