Arterial spin labelling (ASL) is a noninvasive imaging modality, used in the clinic and in research, which can give quantitative measurements of perfusion in the brain and other organs. However, because the signal-to-noise ratio is inherently low and the ASL acquisition is particularly prone to corruption by artifact, image processing methods such as denoising and artifact filtering are vital for generating accurate measurements of perfusion. In this work, we present a new simultaneous approach to denoising and artifact removal, using a novel deep convolutional joint filter architecture to learn and exploit spatio-temporal properties of the ASL signal. We proceed to show, using data from 15 healthy subjects, that our approach achieves state of the art performance in both denoising and artifact removal, improving peak signal-to-noise ratio by up to 50%. By allowing more accurate estimation of perfusion, even in challenging datasets, this technique offers an exciting new approach for ASL pipelines, and might be used both for improving individual images and to increase the power of research studies using ASL
