Dr Jose Bernal Moyano defends his PhD thesis
Dr Jose Bernal Moyano has recently successfully defended his PhD thesis. His PhD project titled "Analysis and processing of dynamic and structural magnetic resonance imaging signals for studying small vessel disease" was supervised by Dr Maria Valdés-Hernández, Dr Javier Escudero, Professor Rhian Touyz, and Professor Joanna Wardlaw.
About Dr Bernal
Jose graduated with merits with a BSc in Computer Engineering from the Universidad del Valle in 2014. He received a recognition of best Computer Engineer graduate in 2014 for his meritorious final career project. A year after, he was awarded an Erasmus Mundus Joint Masters Degree Programme scholarship to complete his MSc studies in Computer Vision and Robotics at the Heriot-Watt University (UK), Universitat de Girona (Spain) and Université de Bourgogne (France). He completed the programme with merits in 2017. His work was recognised for the best masters thesis 2017. He graduated with distinction from HWU and 'mention très bien' from Université de Bourgogne.
Jose has worked previously on artificial intelligence for tissue segmentation and atrophy quantification in brain magnetic resonance imaging at the Universitat de Girona. As well as being part of the Small Vessel Research Group at the University of Edinburgh, during his PhD he also closely worked with the Fondation Leducq Perivascular Spaces in Small Vessel Disease research consortium. Up until now, Dr Bernal has co-authored 45 publications in peer-reviewed scientific journals/book chapters and presented his work at national and international conferences. He was also an author of a dataset available at Edinburgh DataShare.
Dr Bernal has now moved on to a new role as a postdoctoral researcher at the DZNE Magdeburg (Germany), investigating the role of lifestyle factors on MR-based biomarkers of brain health function.
About Dr Bernal's PhD project
The aim of Jose's PhD research project was to propose computational models and signal processing techniques to enable objective examination of post-processing schemes, and improve the quantification of subtle and clinically relevant neuroimaging features of small vessel disease. He provided a summary of his work:
Cerebral small vessel disease (SVD) 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, seems 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 SVD in vivo in humans via magnetic resonance imaging (MRI) challenging and sub-optimal signal processing schemes likely detrimental.
In this doctoral thesis, we proposed computational models and signal processing techniques to improve the quantification and characterisation of subtle and clinically relevant neuroimaging features of SVD. He applied his proposals to analyses of structural MRI (sMRI) and dynamic-contrast enhanced MRI (DCE-MRI) to better characterise SVD.
DCE-MRI is commonly used to investigate cerebrovascular dysfunction, but the extremely subtle nature of the signal in SVD 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 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 SVD, 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 thesis provides means for understanding the effect of image acquisition and processing on the assessment of subtle markers of brain health to maximise confidence in studies of cerebrovascular damage and brain waste clearance via MR. It also constitutes a cornerstone upon which future optimisation and development can be based.
The project was funded by the Precision Medicine Doctoral Training Programme - a fully-funded PhD with integrated study, funded by the Medical Research Council, University of Edinburgh and University of Glasgow.
When asked how he feels about his studies with the Small Vessel Disease Research Group, Dr Bernal replied:
I am extremely honoured, humbled, grateful, and joyful to have completed my doctoral studies at The University of Edinburgh and the University of Glasgow under the supervision of four outstanding researchers who accompanied, guided, and supported me throughout this process: Dr Maria Valdés-Hernández, Dr Javier Escudero, Professor Rhian Touyz, and Professor Joanna Wardlaw. The continuous feedback I received from them and their collaborators, including the SVD Research group, allowed me to grow not only as a scholar but as a professional and as a person, too. Working alongside professionals from various disciplines allowed me to understand that this is precisely the way to address extremely complex health problems that transcend individual research areas.
The holistic and unique training I received as a member of the SVD Research group allowed me to grow academically, personally, and professionally, and to understand that this is precisely the way to address extremely complex health problems
Opportunities at the Row Fogo Centre for Research into Ageing and the Brain - links to related websites