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Abstract #3299

A Learning-From-Noise Dilated Wide Activation Network for Denoising Arterial Spin Labeling (ASL) Perfusion Images

Danfeng Xie1, Yiran Li1, Hanlu Yang1, Li Bai1, and Ze Wang2
1Temple University, Philadelphia, PA, United States, 2University of Maryland School of Medicine, Philadelphia, MD, United States

In this study, we showed that without a noise-free reference, Deep Learning based ASL denoising network can produce cerebral blood flow images with higher signal-to-noise-ratio (SNR) than the reference. In this learning-from-noise training scheme, cerebral blood flow images with very high noise level can be used as reference during network training. This will remove any deliberate pre-processing step for getting the quasi-noise-free reference when training deep learning neural networks. Experimental results this learning-from-noise training scheme preserved the genuine cerebral blood flow information of individual subjects while suppressed noise.

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