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Anatomy-driven modelling of spatial correlation for regularisation of arterial spin labelling images

Research output: Chapter in Book/Report/Conference proceedingConference paper

David Owen, Andrew Melbourne, Zach Eaton-Rosen, David L. Thomas, Neil Marlow, Jonathan Rohrer, Sebastien Ourselin

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings
PublisherSpringer Verlag
Number of pages8
Volume10434 LNCS
ISBN (Print)9783319661841
Publication statusPublished - 1 Jan 2017
Event20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 - Quebec City, Canada
Duration: 11 Sep 201713 Sep 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10434 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
CityQuebec City


King's Authors


Arterial spin labelling (ASL) allows blood flow to be measured in the brain and other organs of the body, which is valuable for both research and clinical use. Unfortunately, ASL suffers from an inherently low signal to noise ratio, necessitating methodological advances in ASL acquisition and processing. Spatial regularisation improves the effective signal to noise ratio, and is a common step in ASL processing. However, the standard spatial regularisation technique requires a manually-specified smoothing kernel of an arbitrary size, and can lead to loss of fine detail. Here, we present a Bayesian model of spatial correlation, which uses anatomical information from structural images to perform principled spatial regularisation, modelling the underlying signal and removing the need to set arbitrary smoothing parameters. Using data from a large cohort (N = 130) of preterm-born adolescents and age-matched controls, we show our method yields significant improvements in test-retest reproducibility, increasing the correlation coefficient by 14% relative to Gaussian smoothing and giving a corresponding improvement in statistical power. This novel technique has the potential to significantly improve single inversion time ASL studies, allowing more reliable detection of perfusion differences with a smaller number of subjects.

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