ICA-based denoising for ASL perfusion imaging

Carone, D.; Harston, G.W.J.; Garrard, J.; De Angeli, F.; Griffanti, L.; Okell, T.W.; Chappell, M.A.; Kennedy, J.

Abstract

Arterial Spin Labelling (ASL) imaging derives a perfusion image by tracing the accumulation of magnetically labeled blood water in the brain. As the image generated has an intrinsically low signal to noise ratio (SNR), multiple measurements are routinely acquired and averaged, at a penalty of increased scan duration and opportunity for motion artefact. However, this strategy alone might be ineffective in clinical settings where the time available for acquisition is limited and patient motion are increased. This study investigates the use of an Independent Component Analysis (ICA) approach for denoising ASL data, and its potential for automation.
72 ASL datasets (pseudo-continuous ASL; 5 different post-labeling delays: 400, 800, 1200, 1600, 2000 m s; total volumes = 60) were collected from thirty consecutive acute stroke patients. The effects of ICA-based denoising (manual and automated) where compared to two different denoising approaches, aCompCor, a Principal Component-based method, and Enhancement of Automated Blood Flow Estimates (ENABLE), an algorithm based on the removal of corrupted volumes. Multiple metrics were used to assess the changes in the quality of the data following denoising, including changes in cerebral blood flow (CBF) and arterial transit time (ATT), SNR, and repeatability. Additionally, the relationship between SNR and number of repetitions acquired was estimated before and after denoising the data.
The use of an ICA-based denoising approach resulted in significantly higher mean CBF and ATT values (p [less than] 0.001), lower CBF and ATT variance (p [less than] 0.001), increased SNR (p [less than] 0.001), and improved repeatability (p [less than] 0.05) when compared to the raw data. The performance of manual and automated ICA-based denoising was comparable. These results went beyond the effects of aCompCor or ENABLE. Following ICA-based denoising, the SNR was higher using only 50% of the ASL-dataset collected than when using the whole raw data.
The results show that ICA can be used to separate signal from noise in ASL data, improving the quality of the data collected. In fact, this study suggests that the acquisition time could be reduced by 50% without penalty to data quality, something that merits further study. Independent component classification and regression can be carried out either manually, following simple criteria, or automatically.

• Journal Article Type: Article
• Acceptance Date: Jul 1, 2019
• Online Publication Date: Jul 2, 2019
• Publication Date: Oct 15, 2019
• Deposit Date: Sep 8, 2020
• Publicly Available Date: Sep 11, 2020
• Journal: NeuroImage
• Print ISSN: 1053-8119
• Electronic ISSN: 1095-9572
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 200
• Pages: 363-372
• DOI: https://doi.org/10.1016/j.neuroimage.2019.07.002
• Keywords: Cognitive Neuroscience; Neurology
• Public URL: https://nottingham-repository.worktribe.com/output/4889211
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S1053811919305646
• Additional Information: This article is maintained by: Elsevier; Article Title: ICA-based denoising for ASL perfusion imaging; Journal Title: NeuroImage; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.neuroimage.2019.07.002; Content Type: article; Copyright: Crown Copyright © 2019 Published by Elsevier Inc.