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