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Intelligent Imaging of Perfusion Using Arterial Spin Labelling

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Intelligent Imaging of Perfusion Using Arterial Spin Labelling Intelligent Imaging of Perfusion Using Arterial Spin Labelling David Owen A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy of University College London. Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering University College London February 24, 2020 2 I, David Owen, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indi- cated in the work. Abstract Arterial spin labelling (ASL) is a powerful magnetic resonance imaging technique, which can be used to noninvasively measure perfusion in the brain and other organs of the body. Promising research results show how ASL might be used in stroke, tumours, dementia and paediatric medicine, in addition to many other areas. How- ever, significant obstacles remain to prevent widespread use: ASL images have an inherently low signal to noise ratio, and are susceptible to corrupting artifacts from motion and other sources. The objective of the work in this thesis is to move towards an “intelligent imaging” paradigm: one in which the image acquisition, reconstruc- tion and processing are mutually coupled, and tailored to the individual patient. This thesis explores how ASL images may be improved at several stages of the imaging pipeline. We review the relevant ASL literature, exploring details of ASL acquisitions, parameter inference and artifact post-processing. We subsequently present original work: we use the framework of Bayesian experimental design to generate optimised ASL acquisitions, we present original methods to improve pa- rameter inference through anatomically-driven modelling of spatial correlation, and we describe a novel deep learning approach for simultaneous denoising and artifact filtering. Using a mixture of theoretical derivation, simulation results and imaging experiments, the work in this thesis presents several new approaches for ASL, and hopefully will shape future research and future ASL usage. Impact Statement In an academic context, the work presented herein offers three princi- pal benefits within the area of ASL imaging. First, the novel exper- imental design approach for optimising ASL acquisitions offers state of the art results compared to previous comparable work, and is gen- erally applicable to different ASL acquisitions. Moreover, this work has helped to revive the idea of experimental design for ASL acquisi- tions, as well as presenting a more extensive validation across different ASL acquisitions. Second, the use of shrinkage priors offers a step for- ward in ASL image processing generally, and may be applied to any of the popular models used for inference. Third, the use of a convolu- tional neural network for joint denoising and artifact removal presents an exciting new direction in imaging research, and is among the ear- liest such work. Much of this work has impact in academia outside the specific area of ASL imaging. The experimental design approach developed herein is generally applicable to imaging modalities with a tractable signal and suitable noise model, and has already been used in abdominal imaging and experimental multi-modal acquisitions in both human and animal experiments. The shrinkage prior approach bridges the gap between the emerging field of probabilistic programming – in which multi-level regression is a canonical technique for data analy- sis – and more traditional numerical methods for Bayesian inference on otherwise-intractable models. Finally, the convolutional neural net- work approach to joint denoising and artifact removal is generally ap- Impact Statement 5 plicable to other imaging modalities, including natural images. Finally, there is potential for impact outside academia. Most obviously, many of the methods developed in this work may, one day, be suitable for clinical use. Optimised acquisitions, improved fitting and improved fil- tering of artifacts all have a place in clinical ASL (as well as in other imaging modalities). Parts of the work in this thesis contributed to the open-source software, NiftyFit, a package for multi-modal MRI fitting. Acknowledgements Karolina, I cannot express how grateful I am for your generosity, patience and love. My heartfelt thanks go to my wonderful supervisors. Andrew, thank you so much for seeing me through this. Dave, I can’t believe in retrospect how lucky I was to end up with you as a supervisor. Jon, thank you so much for always pushing my work forward, getting me data, and keeping the work grounded. Seb, thank you for bringing me to London, persuading me to do a PhD, and creating this wonderful environment for research. I must also give an honourary mention to Enrico, who gave significant input on the optimal design work. Thanks to my cherished peers, collaborators and friends: Michael, Catherine, Zach, Alex, Carla, Michela, and everyone else at TIG and CMIC. Thanks to my family, who have supported me through years of conference deadlines, flying visits and thesis writing: Mum, Dad, Alan; Helen, Katy, Louise; Grandad. Thanks to my non-imaging friends, who bore with me as I attempted this mad task and basically ignored them for the past six months: George, Bob, Jon, Angela, Josh, Merlyn, Michael, Oli, Martin, Mo, Laura, Will, Friend. I also thank those who, sadly, had to leave: Nan, Grandma, Sid, Joan, Jenny. Finally, thanks to the Wolfson Foundation and EPSRC for funding my work. Publications and abstracts As first author • D. Owen, A. Melbourne, Z. Eaton-Rosen, D. L. Thomas, N. Marlow, J. Rohrer, and S. Ourselin. Deep convolutional filtering for spatio-temporal denoising and artifact removal in arterial spin labelling MRI. In International Conference on Medical Image Computing and Computer-Assisted Interven- tion, pages 21–29. Springer, 2018 • D. Owen, A. Melbourne, Z. Eaton-Rosen, D. L. Thomas, N. Marlow, J. Rohrer, and S. Ourselin. Anatomy-driven modelling of spatial correlation for regularisation of arterial spin labelling images. In International Con- ference on Medical Image Computing and Computer-Assisted Intervention, pages 190–197. Springer, 2017 • D. Owen, A. Melbourne, D. Thomas, J. Beckmann, J. Rohrer, N. Marlow, and S. Ourselin. ADRIMO: Anatomy-DRIven MOdelling of spatial correlation to improve analysis of arterial spin labelling data. In Proceedings of the Annual Meeting of the International Society for Magnetic Resonance in Medicine, Honolulu, Hawaii. ISMRM, 2017 • D. Owen, A. Melbourne, M. Sokolska, D. Thomas, J. Rohrer, and S. Ourselin. Bayesian experimental design for multi-parametric T1/T2 relaxometry and diffusion. In Proceedings of the Annual Meeting of the International Society for Magnetic Resonance in Medicine, Honolulu, Hawaii. ISMRM, 2017 • D. Owen, A. Melbourne, D. Thomas, E. De Vita, J. Rohrer, and S. Ourselin. Acknowledgements 8 Optimisation of arterial spin labelling using Bayesian experimental design. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016. Springer, 2015 As a coauthor • A. Melbourne, R. Aughwane, M. Sokolska, D. Owen, G. Kendall, D. Flouri, A. Bainbridge, D Atkinson, J. Deprest, T. Vercauteren, et al. Separating fe- tal and maternal placenta circulations using multiparametric MRI. Magnetic resonance in medicine, 81(1):350–361, 2019 • L. Smith, A. Melbourne, D. Owen, M. J. Cardoso, C. Sudre, T. Tillin, M. Sokolska, D. Atkinson, N. Chaturvedi, S. Ourselin, A. Hughes, F. Barkhof, and R. Jäger. Cortical cerebral blood flow in aging: Effects of haematocrit, sex and ethnicity. European Radiology, 2019 • R. Pratt, A. Melbourne, D. Owen, M. Sokolska, A. Bainbridge, D. Atkinson, J. Deprest, G. Kendall, T. Vercauteren, S. Ourselin, et al. Spatial vascular heterogeneity in the normal placenta assessed with multicompartment pla- cental mri. In Proceedings of the Annual Meeting of the International Society for Magnetic Resonance in Medicine, Paris, France. Joint Annual Meeting ISMRM-ESMRMB 2018, 2018 • L. Smith, A. Melbourne, D. Owen, MJ. Cardoso, C. Sudre, T. Tillin, M. Sokolska, D. Atkinson, N. Chaturvedi, S. Ourselin, et al. Cortical cerebral blood flow in aging: Effects of haematocrit, sex and ethnicity. In Proceedings of the Annual Meeting of the International Society for Magnetic Resonance in Medicine, Paris, France. Joint Annual Meeting ISMRM-ESMRMB 2018, 2018 • A. Melbourne, R. Pratt, D. Owen, M. Sokolska, A. Bainbridge, D. Atkinson, J. Deprest, G. Kendall, T. Vercauteren, A. David, et al. Placental insuffi- ciency investigated with multi-compartment placental MRI. In Proceedings of the Annual Meeting of the International Society for Magnetic Resonance Acknowledgements 9 in Medicine, Paris, France. Joint Annual Meeting ISMRM-ESMRMB 2018, 2018 • R. Pratt, A. Melbourne, D. Owen, M. Sokolska, A. Bainbridge, D. Atkinson, G. Kendall, J. Deprest, T. Vercauteren, S. Ourselin, et al. Novel placental evaluation using multimodal MRI. Ultrasound in Obstetrics & Gynecology, 50(S1):62–62, 2017 • A. Melbourne, R. Pratt, M. Sokolska, D. Owen, A. Bainbridge, D. Atkinson, G. Kendall, J. Deprest, T. Vercauteren, A. David, et al. Separation of fetal and maternal circulations using multi-modal MRI. Placenta, 57:291, 2017 • A. Melbourne, R. Pratt, D. Owen, M. Sokolska, A. Bainbridge, D. Atkinson, G. Kendall, J. Deprest, T. Vercauteren, A. David, et al. DECIDE: Diffusion- rElaxation combined imaging for detailed placental evaluation. In Proceed- ings of the Annual Meeting of the International Society for Magnetic Reso- nance in Medicine, Honolulu, Hawaii. ISMRM, 2017
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