Keywords: fMRI Analysis, Perfusion
Motivation: Test a denoising method, block matching with 4D filtering (BM4D), to improve the performance of a low signal-to-noise modality, functional imaging using arterial spin labeling (ASL)
Goal(s): Apply BM4D to improve the detection of brain activity
Approach: Use task-based fMRI in human data and in simulation to test how well the BM4D denoised data corresponds to ground truth activation compared to non-denoised data
Results: The BM4D denoised data performs better than non-denoised data when spatial Gaussian smoothing is not used prior to analysis but fails to outperform when adequate spatial Gaussian smoothing is used.
Impact: For functional arterial spin labeling (ASL), denoising algorithms may show an improvement in detecting brain activity in simulation, but not when comparisons are made after adequate spatial Gaussian smoothing.
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