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Arterial spin labeling (ASL) perfusion MRI provides a non-invasive way toquantify cerebral blood flow (CBF) but it still suffers from a lowsignal-to-noise-ratio (SNR). Using deep machine learning (DL), several groupshave shown encouraging denoising results. Interestingly, the improvement wasobtained when the deep neural network was trained using noise-contaminatedsurrogate reference because of the lack of golden standard high quality ASL CBFimages. More strikingly, the output of these DL ASL networks (ASLDN) showedeven higher SNR than the surrogate reference. This phenomenon indicates alearning-from-noise capability of deep networks for ASL CBF image denoising,which can be further enhanced by network optimization. In this study, weproposed a new ASLDN to test whether similar or even better ASL CBF imagequality can be achieved in the case of highly noisy training reference.Different experiments were performed to validate the learning-from-noisehypothesis. The results showed that the learning-from-noise strategy producedbetter output quality than ASLDN trained with relatively high SNR reference.

A Learning-from-noise Dilated Wide Activation Network for denoising Arterial Spin Labeling (ASL) Perfusion Images