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Abstract #0736

Optimizing arterial spin labeling denoising with deep learning – effects of averaging and training strategies

Jia Guo1, Arun Sharma2, and Hossein Rahimzadeh1
1Bioengineering, University of California, Riverside, Riverside, CA, United States, 2Electrical Engineering, University of California, Riverside, Riverside, CA, United States

Synopsis

Keywords: Arterial Spin Labelling, Arterial spin labelling, Deep Learning, Denoising, Optimization

Motivation: Optimizing deep-learning based denoising of ASL is of great interest, but has not been systematically studied.

Goal(s): To investigate the effects of different averaging methods on deep-learning based denoising for optimized performance.

Approach: ASL datasets of different SNR levels were created using different levels of averaging and methods (windowed vs. interleaved). Various full-3D Unet-based models were trained and compared. The effect of including the M0 scan was also studied.

Results: Optimal denoising performance was observed when the SNR levels were matched in training and testing, with windowed averaging. Including M0 can provide additional improvement, especially at low SNR.

Impact: This is the first study to systematically investigate the effects of different averaging and training strategies in DL-based ASL denoising. The results will help guide ASL scan and post-processing designs for significant reduction of scan time while achieving optimal performance.

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Keywords