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Deep convolutional filtering for spatio-temporal denoising and artifact removal in arterial spin labelling MRI

Owen, D; Melbourne, A; Eaton-Rosen, Z; Thomas, DL; Marlow, N; Rohrer, J; Ourselin, S; (2018) Deep convolutional filtering for spatio-temporal denoising and artifact removal in arterial spin labelling MRI. In: Frangi, A and Schnabel, J and Davatzikos, C and Alberola-López, C and Fichtinger, G, (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. (pp. pp. 21-29). Springer: Cham, Switzerland. Green open access

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Abstract

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.

Type: Proceedings paper
Title: Deep convolutional filtering for spatio-temporal denoising and artifact removal in arterial spin labelling MRI
Event: 21st Medical Image Computing and Computer-Assisted Intervention International Conference, 16-20 September 2018, Granada, Spain,
ISBN-13: 9783030009274
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-00928-1_3
Publisher version: https://doi.org/10.1007/978-3-030-00928-1_3
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Brain Repair and Rehabilitation
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Neurodegenerative Diseases
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health > Neonatology
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10060671
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