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Abstract #0659

A deep learning approach to estimate voxelwise cardiac-related brain pulsatility from BOLD MRI

Nicholas J Luciw1,2, William Cameron2, Andrew D Robertson3, Sarah Atwi2, and Bradley J MacIntosh1,2
1Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada, 2Sunnybrook Research Institute, Toronto, ON, Canada, 3Department of Kinesiology, University of Waterloo, Waterloo, ON, Canada

Blood-oxygenation-level-dependent (BOLD) MRI contains both neuronally-mediated and physiological contrast, such as cardiac-related hemodynamic pulsatility. Without a coincident cardiac trace, however, few options exist to assess voxel-specific hemodynamic pulsatility. We investigated the feasibility of training a convolutional neural network to generate accurate cardiac-related pulsatility maps without cardiac trace recordings. Using features derived from the BOLD signal, the network produced pulsatility estimates that were significantly associated with ground truth. This automated method enables investigation of cerebrovascular conditions through the vascular contributions to BOLD data, specifically when cardiac trace recordings are unavailable.

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