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Selective Motion Artefact Reduction via Radiomics and k-space Reconstruction for Improving Perivascular Space Quantification in Brain Magnetic Resonance Imaging

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12722)

Abstract

Current evidence points towards perivascular spaces playing a key role in cerebral haemodynamics and waste clearance. Hence, their precise quantification may become a powerful tool for assessing brain health and further establishing their relationship with neurological diseases. Large strides have been made towards developing automatic tools to computationally assess the burden of perivascular spaces in MRI in recent years. However, their applicability depends to a large extent on the quality of the images. In this paper, we propose a pipeline to improve perivascular space quantification by means of radiomics-based image quality control and selective motion artefacts reduction. We demonstrate our method on a sample of patients with mild stroke (n = 60) with different extents of small vessel disease features and image quality. We show our proposal can differentiate high- and low-quality scans (AUROC = 0.98) and reduce imaging artefacts, which leads to greater correlations between visual and computational measurements, especially in the centrum semiovale (polyserial correlation: 0.86 [95% CI 0.85, 0.88] and 0.17 [95% CI 0.14, 0.21] with and without our proposal, respectively). Our preliminary results demonstrate the potential of our proposal for retaining clinically relevant information while reducing imaging artefacts.

Keywords

Perivascular spaces Cerebral small vessel disease Image enhancement Imaging artefact reduction Brain magnetic resonance imaging 

Notes

Acknowledgements

This work is supported by: MRC Doctoral Training Programme in Precision Medicine (JB - Award Reference No. 2096671); the UK Dementia Research Institute which receives its funding from DRI Ltd, funded by the UK MRC, Alzheimer’s Society and Alzheimer’s Research UK; the Foundation Leducq Network for the Study of Perivascular Spaces in Small Vessel Disease (16 CVD 05); Stroke Association ‘Small Vessel Disease-Spotlight on Symptoms (SVD-SOS)’ (SAPG 19\100068); The Row Fogo Charitable Trust Centre for Research into Aging and the Brain (MVH) (BRO-D.FID3668413); Stroke Association Garfield Weston Foundation Senior Clinical Lectureship (FND) (TSALECT 2015/04); NHS Research Scotland (FND); British Heart Foundation Edinburgh Centre for Research Excellence (RE/18/5/34216); a British Heart Foundation Chair award (RMT) (CH/12/4/29762); NHS Lothian Research and Development Office (MJT); European Union Horizon 2020, PHC-03-15, project No666881, ‘SVDs@Target’ (MS); Chief Scientist Office of Scotland Clinical Academic Fellowship (UC) (CAF/18/08); Stroke Association Princess Margaret Research Development Fellowship (UC) (2018); Alzheimer Nederland (ACCJ). The Research MR scanners are supported by the Scottish Funding Council through the Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration; the 3T scanner is funded by the Wellcome Trust (104916/Z/14/Z), Dunhill Trust (R380R/1114), Edinburgh and Lothians Health Foundation (2012/17), Muir Maxwell Research Fund, and the University of Edinburgh. We thank the participants, their families, radiographers at Edinburgh Imaging Facility Royal Infirmary of Edinburgh, and the Stroke Research Network at the University of Edinburgh.

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© Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.Centre for Clinical Brain SciencesThe University of EdinburghEdinburghUK
  2. 2.Institute for Digital CommunicationsThe University of EdinburghEdinburghUK
  3. 3.Institute of Cardiovascular and Medical SciencesUniversity of GlasgowGlasgowUK

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