  Arterial spin labeling perfusion MRI is a noninvasive technique for measuring
quantitative cerebral blood flow (CBF), but the measurement is subject to a low
signal-to-noise-ratio(SNR). Various post-processing methods have been proposed
to denoise ASL MRI but only provide moderate improvement. Deep learning (DL) is
an emerging technique that can learn the most representative signal from data
without prior modeling which can be highly complex and analytically
indescribable. The purpose of this study was to assess whether the record
breaking performance of DL can be translated into ASL MRI denoising. We used
convolutional neural network (CNN) to build the DL ASL denosing model (DL-ASL)
to inherently consider the inter-voxel correlations. To better guide DL-ASL
training, we incorporated prior knowledge about ASL MRI: the structural
similarity between ASL CBF map and grey matter probability map. A relatively
large sample data were used to train the model which was subsequently applied
to a new set of data for testing. Experimental results showed that DL-ASL
achieved state-of-the-art denoising performance for ASL MRI as compared to
current routine methods in terms of higher SNR, keeping CBF quantification
quality while shorten the acquisition time by 75%, and automatic partial volume
correction.
