Show simple item record

dc.contributor.advisorValdes Hernandez, Maria
dc.contributor.advisorEscudero Rodriguez, Javier
dc.contributor.advisorWardlaw, Joanna
dc.contributor.authorBernal Moyano, Jose
dc.date.accessioned2022-06-23T10:55:28Z
dc.date.available2022-06-23T10:55:28Z
dc.date.issued2022-07-08
dc.identifier.urihttps://hdl.handle.net/1842/39186
dc.identifier.urihttp://dx.doi.org/10.7488/era/2437
dc.description.abstractCerebral small vessel disease (CSVD) describes multiple and dynamic pathological processes disrupting the optimum functioning of perforating arterioles, capillaries and venules, increasing the risk of stroke and dementia. Although the pathogenesis of this disease is still elusive, the breakdown of the blood-brain barrier (BBB), which would hinder brain waste clearance, is thought to play a pivotal factor in it. Nonetheless, the microscopic origin and nature of these abnormalities and the lack of a ground truth make the study of CSVD in vivo in humans via magnetic resonance imaging (MRI) challenging and signal processing schemes likely to be sub-optimal. In this doctoral thesis, we proposed signal analysis and processing techniques to improve the quantification and characterisation of subtle and clinically relevant neuroimaging features of CSVD. We applied our proposals to analyses of structural and dynamic-contrast enhanced MRI (sMRI and DCE-MRI) to better characterise CSVD. DCE-MRI is commonly used to investigate cerebrovascular dysfunction, but the extremely subtle nature of the signal in CSVD makes it unclear whether signal changes are caused by microscopic yet critical BBB abnormalities. Moreover, ethical and safety considerations in vivo and the lack of validation frameworks hinder optimising imaging protocols and processing schemes. To cope with these issues, we thus proposed an open-source computational human brain model for mimicking the four-dimensional DCE-MRI acquisition process. With it, we quantified the substantial impact of spatiotemporal considerations on permeability mapping, detected sources of errors that had been overlooked in the past, and provided evidence of the harmful effect of post-processing or lack thereof on DCE-MRI assessments. Perivascular spaces (PVS) in the brain, which are involved in brain waste clearance, can become visible in sMRI scans of patients with neuroimaging features of CSVD, but their automatic quantification is challenging due to the size of PVS, the incidence and presence of imaging artefacts, and the lack of a ground truth. We first proposed a computational model of sMRI to study and compare current PVS segmentation techniques and identify major areas of improvement. We confirmed that optimal segmentation requires tuning depending on image quality and that motion artefacts are particularly detrimental to PVS quantification. We then proposed a processing strategy that distinguished high-quality from motion-corrupted images and processed them accordingly. We demonstrated such an approximation leads to estimates that correlate better with clinical visual scores and agree more with full manual counts. After optimisation using our proposals, we also found PVS measurements were associated with BBB permeability, in accordance with the link between brain waste clearance and endothelial dysfunction. This work provides means for understanding the effect of image acquisition and processing on the assessment of subtle markers of brain health to maximise confidence of studies of endothelial dysfunction and brain waste clearance via MRI. It also constitutes a cornerstone on which future optimisation and development can be based upon.en
dc.language.isoenen
dc.publisherThe University of Edinburghen
dc.relation.hasversionBernal-Moyano, J.A., Kushibar, K., Asfaw, D.S., Valverde, S., Oliver, A., Lladó, X., 2017. Deep convolutional neural networks for brain Image analysis in magnetic resonance imaging : A review 1–37.en
dc.relation.hasversionBernal, J., Valdés-Hernández, M.d.C., Escudero, J., Heye, A.K., Sakka, E., Armitage, P.A., Makin, S., Touyz, R.M. and Wardlaw, J.M. and Thrippleton, M.J. (2021). A four-dimensional computational model of dynamic contrast-enhanced magnetic resonance imaging measurement of subtle blood-brain barrier leakage. NeuroImage, 230, 117786. https://doi.org/10.1016/j.neuroimage.2021.117786en
dc.relation.hasversionBernal, J., Valdés-Hernández, M.d.C., Escudero, J., Sakka, E., Armitage, P.A., Makin, S., Touyz, R.M. and Wardlaw, J.M. (2020). Examining the relationship between semiquantitative methods analysing concentration time and enhancement-time curves from DCE-MRI and cerebrovascular dysfunction in small vessel disease. Journal of Imaging, 6(6), 43. https://doi.org/10.3390/jimaging6060043en
dc.relation.hasversionBernal, J., Valdés-Hernández, M.d.C., Escudero, J., Viksne, L., Heye, A.K., Armitage, P.A., Makin, S., Touyz, R.M. and Wardlaw, J.M. (2020). Analysis of dynamic descriptors of dynamic contrast-enhanced brain magnetic resonance images for studying small vessel disease. Magnetic Resonance Imaging, 66, 240-247. https://doi.org/10.1016/j.mri.2019.11.001en
dc.relation.hasversionBernal, J., Valdés-Hernández, M.d.C., Escudero, J., Duarte, R., Ballerini, L., Bastin, M.E., Deary, I., Thrippleton, M.J., Touyz, R.M. and Wardlaw, J.M. and Thrippleton, M.J. (2022). Assessment of perivascular space enhancement methods using a three-dimensional computational model, Preprints, 2022040058. https://doi.org/10.20944/preprints202204.0058.v1en
dc.relation.hasversionBernal, J., Xu, W., Valdés-Hernández, M.d.C., Escudero, J., Jochems, A.C., Clancy, U., Doubal, F.N., Stringer, M.S., Thrippleton, M.J., Touyz, R.M. and Wardlaw, J.M. (2021). Selective motion artefact reduction via radiomics and k-space reconstruction for improving perivascular space quantification in brain magnetic resonance imaging. In: Papież, B., Namburete, A., Yaqub, M., Noble, J. (eds) Medical Image Understanding and Analysis. MIUA 2021. Lecture Notes in Computer Science, 12722. Springer, Cham. https://doi.org/10.1007/978-3-030-80432-9_12en
dc.relation.hasversionBernal, J., Valdés-Hernández, M.d.C., Ballerini, L., Escudero, J., Jochems, A.C., Clancy, U., Doubal, F.N., Stringer, M.S., Thrippleton, M.J.,Touyz, R.M. and Wardlaw, J.M. (2020). 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., Namburete, A., Yaqub, M., Noble, J. (eds) Medical Image Understanding and Analysis. MIUA 2020. Communications in Computer and Information Science, 1248, 171-183. Springer, Cham. https://doi.org/10.1007/978-3- 030-52791-4_14en
dc.relation.hasversionBernal, J., Valdés-Hernández, M.d.C., Escudero, J., Heye, A.K., Armitage, P.A., Makin, S., Touyz, R.M. and Wardlaw, J.M. (2019). Analysis of spatial spectral features of dynamic contrast-enhanced brain magnetic resonance images for studying small vessel disease. In: Zheng, Y., Williams, B., Chen, K. (eds) Medical Image Understanding and Analysis. MIUA 2019. Communications in Computer and Information Science, 1065, 282-293. Springer, Cham. https://doi.org/10.1007/978-3-030-39343-4_24en
dc.relation.hasversionBernal, J., Valdés-Hernández, M.d.C., Escudero, J., Armitage, P.A., Makin, S., Touyz, R.M. and Wardlaw, J.M. (2020) Voxel-based analysis of white matter hyperintensity signal changes one year after the ischaemic stroke. International Journal of Stroke, 15 (IS), 573. https://doi.org/10.1177/1747493020963387en
dc.relation.hasversionBernal, J., Valdés-Hernández, M.d.C., Escudero, J., Heye, A.K., Armitage, P.A., Makin, S., Touyz, R.M. Wardlaw, J.M., and Thrippleton, M.J. (2020). A novel digital reference object for DCE-MRI measurement of subtle blood brain barrier leakage. Organization for Human Brain Mapping. https://doi.org/10.13140/RG.2.2.23989.50406en
dc.relation.hasversionBernal, J., Valdés-Hernández, M.d.C., Escudero, J., Heye, A.K., Sakka, E., Armitage, P.A., Makin, S., Touyz, R.M. and Wardlaw, J.M. and Thrippleton, M.J. (2020). A four-dimensional computational model of dynamic contrast-enhanced magnetic resonance imaging measurement of subtle blood-brain barrier leakage: source code. The University of Edinburgh. https://doi.org/10.7488/ds/2966en
dc.relation.hasversionBernal, J., Valdés-Hernández, M.d.C., Escudero, J., Heye, A.K., Armitage, P.A., Makin, S., Touyz, R.M. Wardlaw, J.M., and Thrippleton, M.J. (2021). Systematic review of signal post-processing methods in blood-brain barrier dysfunction assessments via dynamic-contrast enhanced magnetic resonance imaging. The University of Edinburgh. https://doi.org/10.7488/ds/3039en
dc.relation.hasversionCarvajal-Camelo, E. E., Bernal, J., Oliver, A., Lladó, X., and Trujillo, M. (2021). Evaluating the Effect of Intensity Standardisation on Longitudinal Whole Brain Atrophy Quantification in Brain Magnetic Resonance Imaging Applied Sciences, 11(4), 1773. https://doi.org/10.3390/app11041773en
dc.relation.hasversionCarvajal-Camelo, E. E., Bernal, J., and Trujillo, M. (2021). Improving longitudinal whole brain atrophy quantification in brain magnetic resonance imaging through retrospective intensity standardisation. MAGMA, 33, S204. https://doi.org/10.1007/s10334-020-00876-yen
dc.relation.hasversionDiaz, D., Cadena, S., Gil, J., Bernal, J., and Trujillo, M. (2020). A web application for computerised neuropsychological assessments for studying brain activation during fMRI. MAGMA, 33, S91. https://doi.org/10.1007/s10334-020-00876-yen
dc.relation.hasversionBorchert, R., Azevedo, T., Badhwar, A., Bernal, J., Betts, M., Bruffaerts, R., Dewachter, I., Gellersen, H., Low, A., Madan, CR., Malpetti, M., Mejia, J., Michopoulou, S., Neira, C., Peres, M., Phillips, V., Ramanan, S., Tamburin, S., Tantiangco, H., Thakur, L., Tomassini, A., Vipin, A., Tang, E., Newby, D., Ranson, J., Llewellyn, D., Veldsman, M., and Rittman, T. (2021) Artificial intelligence for diagnosis and prognosis in neuroimaging for dementia; a systematic review. medRxiv, 2021.12.12.21267677. https://doi.org/10.1101/2021.12.12.21267677en
dc.relation.hasversionPati, S., Baid, U., Edwards, B., Sheller, M., et al. [including Bernal, J.] (2022). Federated Learning Enables Big Data for Rare Cancer Boundary Detection. arXiv. https://doi.org/10.48550/arXiv.2204.10836en
dc.relation.hasversionMoullaali, T., Walters, R., Samarasekera, N., Lerpiniere, C., Bernal, J., Wang, X., Humphreys, C., Wardlaw, J.M., Smith, C., Anderson, C., Mckinstry, B., and Al-Shahi Salman R. (2020). Association between systolic blood pressure variability and severity of cerebral amyloid angiopathy in incident ICH. International Journal of Stroke, 15 (IS), 571. https://doi.org/10.1177/1747493020963387en
dc.subjectMRI distortion levelsen
dc.subjectperivascular spacesen
dc.subjectbrain waste clearanceen
dc.subjectimaging distortionsen
dc.subjectPVS segmentation techniquesen
dc.subjectPVS quantificationen
dc.subjectimage acquisition and processingen
dc.subjectcerebrovascular damageen
dc.titleAnalysis and processing of dynamic and structural magnetic resonance imaging signals for studying small vessel diseaseen
dc.typeThesis or Dissertationen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD Doctor of Philosophyen


Files in this item

This item appears in the following Collection(s)

Show simple item record