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

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Medical Image Understanding and Analysis (MIUA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12722))

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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.

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Notes

  1. 1.

    Full details of the study protocol and image acquisition are provided in [24].

  2. 2.

    The MRIQC documentation can be found in mriqc.readthedocs.io.

References

  1. Potter, G.M., Chappell, F.M., Morris, Z., Wardlaw, J.M.: Cerebral perivascular spaces visible on magnetic resonance imaging: development of a qualitative rating scale and its observer reliability. Cerebrovasc. Dis. 39(3–4), 224–231 (2015)

    Article  Google Scholar 

  2. Wardlaw, J.M., Smith, C., Dichgans, M.: Small vessel disease: mechanisms and clinical implications. Lancet Neurol. 18(7), 684–696 (2019)

    Article  Google Scholar 

  3. Wardlaw, J.M., et al.: Perivascular spaces in the brain: anatomy, physiology and pathology. Nat. Rev. Neurol. 16(3), 137–153 (2020)

    Article  Google Scholar 

  4. del Maria, C., Hernández, V., Piper, R.J., Wang, X., Deary, I.J., Wardlaw, J.M.: Towards the automatic computational assessment of enlarged perivascular spaces on brain magnetic resonance images: a systematic review: computational assessment of perivascular spaces. J. Magn. Reson. Imaging 38(4), 774–785 (2013). https://doi.org/10.1002/jmri.24047

    Article  Google Scholar 

  5. Ballerini, L., et al.: Computational quantification of brain perivascular space morphologies: associations with vascular risk factors and white matter hyperintensities. A study in the Lothian Birth Cohort 1936. NeuroImage Clin. 25(2019), 102120 (2020)

    Article  Google Scholar 

  6. Bernal, J., et al.: A framework for jointly assessing and reducing imaging artefacts automatically using texture analysis and total variation optimisation for improving perivascular spaces quantification in brain magnetic resonance imaging. In: Papież, B.W., Namburete, A.I.L., Yaqub, M., Noble, J.A. (eds.) MIUA 2020. CCIS, vol. 1248, pp. 171–183. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52791-4_14

    Chapter  Google Scholar 

  7. Esteban, O., Birman, D., Schaer, M., Koyejo, O.O., Poldrack, R.A., Gorgolewski, K.J.: MRIQC: advancing the automatic prediction of image quality in MRI from unseen sites. PLoS ONE 12(9), 1–21 (2017)

    Article  Google Scholar 

  8. Valdés Hernández, M.d.C., et al.: Application of texture analysis to study small vessel disease and blood–brain barrier integrity. Front. Neurol. 8, 327 (2017)

    Google Scholar 

  9. Bernal, J., et al.: Analysis of dynamic texture and spatial spectral descriptors of dynamic contrast-enhanced brain magnetic resonance images for studying small vessel disease. Magn. Reson. Imaging 66, 240–247 (2020)

    Article  Google Scholar 

  10. Atkinson, D., Hill, D.L.G., Stoyle, P.N.R., Summers, P.E., Keevil, S.F.: Automatic correction of motion artifacts in magnetic resonance images using an entropy focus criterion. IEEE Trans. Med. Imaging 16(6), 903–910 (1997)

    Article  Google Scholar 

  11. Zaitsev, M., Maclaren, J., Herbst, M.: Motion artifacts in MRI: a complex problem with many partial solutions. J. Magn. Reson. Imaging 42(4), 887–901 (2015)

    Article  Google Scholar 

  12. Godenschweger, F., et al.: Motion correction in MRI of the brain. Phys. Med. Biol. 61(5), R32–R56 (2017)

    Article  Google Scholar 

  13. Lustig, M., Donoho, D., Pauly, J.M.: Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn. Reson. Med. 58(6), 1182–1195 (2007)

    Article  Google Scholar 

  14. Lamy, J., et al.: Vesselness filters: a survey with benchmarks applied to liver imaging. In: International Conference on Pattern Recognition (2020)

    Google Scholar 

  15. Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A., Delp, S. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0056195

    Chapter  Google Scholar 

  16. Ballerini, L., et al.: Perivascular spaces segmentation in brain MRI using optimal 3D filtering. Sci. Rep. 8(1), 1–11 (2018)

    Article  Google Scholar 

  17. Handley, A., Medcalf, P., Hellier, K., Dutta, D.: Movement disorders after stroke. Age Ageing 38(3), 260–266 (2009)

    Article  Google Scholar 

  18. Bernal, J., et al.: A four-dimensional computational model of dynamic contrast-enhanced magnetic resonance imaging measurement of subtle blood-brain barrier leakage. Neuroimage 230, 117786 (2021). https://doi.org/10.1016/j.neuroimage.2021.117786

    Article  Google Scholar 

  19. Billot, B., Robinson, E., Dalca, A.V., Iglesias, J.E.: Partial volume segmentation of brain MRI scans of any resolution and contrast. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12267, pp. 177–187. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59728-3_18

    Chapter  Google Scholar 

  20. Magnotta, V.A., Friedman, L.: Measurement of signal-to-noise and contrast-to-noise in the fBIRN multicenter imaging study. J. Digit. Imaging 19(2), 140–147 (2006)

    Article  Google Scholar 

  21. Kellman, P., McVeigh, E.R.: Image reconstruction in SNR units: a general method for SNR measurement. Magn. Reson. Med. 54(6), 1439–1447 (2005)

    Article  Google Scholar 

  22. Dietrich, O., Raya, J.G., Reeder, S.B., Reiser, M.F., Schoenberg, S.O.: Measurement of signal-to-noise ratios in MR images: influence of multichannel coils, parallel imaging, and reconstruction filters. J. Magn. Reson. Imaging 26(2), 375–385 (2007)

    Article  Google Scholar 

  23. Mortamet, B., et al.: Automatic quality assessment in structural brain magnetic resonance imaging. Magn. Reson. Med. 62(2), 365–372 (2009)

    Article  Google Scholar 

  24. Clancy, U., et al.: Rationale and design of a longitudinal study of cerebral small vessel diseases, clinical and imaging outcomes in patients presenting with mild ischaemic stroke: mild stroke study 3. Eur. Stroke J. 6(1), 81–88 (2020)

    Article  Google Scholar 

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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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Authors and Affiliations

  1. Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK

    Jose Bernal, William Xu, Maria d. C. Valdés-Hernández, Angela C. C. Jochems, Una Clancy, Fergus N. Doubal, Michael S. Stringer, Michael J. Thrippleton & Joanna M. Wardlaw

  2. Institute for Digital Communications, The University of Edinburgh, Edinburgh, UK

    Javier Escudero

  3. Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK

    Rhian M. Touyz

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  1. Jose Bernal
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Correspondence to Jose Bernal .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Bartłomiej W. Papież

  2. Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates

    Mohammad Yaqub

  3. University of Oxford, Oxford, UK

    Jianbo Jiao

  4. University of Oxford, Oxford, UK

    Ana I. L. Namburete

  5. University of Oxford, Oxford, UK

    J. Alison Noble

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Bernal, J. et al. (2021). Selective Motion Artefact Reduction via Radiomics and k-space Reconstruction for Improving Perivascular Space Quantification in Brain Magnetic Resonance Imaging. In: Papież, B.W., Yaqub, M., Jiao, J., Namburete, A.I.L., Noble, J.A. (eds) Medical Image Understanding and Analysis. MIUA 2021. Lecture Notes in Computer Science(), vol 12722. Springer, Cham. https://doi.org/10.1007/978-3-030-80432-9_12

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  • DOI: https://doi.org/10.1007/978-3-030-80432-9_12

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