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Computational and Experimental Techniques Towards Optimising the Cardiovascular Risk Assessment of Hyperbaric Decompression Stre Imperial College London Department of Bioengineering Computational and experimental techniques towards optimising the cardiovascular risk assessment of hyperbaric decompression stress caused by circulatory bubble dynamics Virginie Papadopoulou Submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy of Imperial College London. 1 2 Declaration of Originality I hereby certify that this thesis is a product of my own work. Where I have consulted the published work of others, this has been appropriately referenced. Where other people have contributed to the work presented, this has been clearly stated and their contributions attributed. Virginie Papadopoulou. 3 4 Copyright Declaration The copyright of this thesis rests with the author and is made available under a Creative Commons Attribution Non-Commercial No Derivatives licence. Researchers are free to copy, distribute or transmit the thesis on the condition that they attribute it, that they do not use it for commercial purposes and that they do not alter, transform or build upon it. For any reuse or redistribution, researchers must make clear to others the licence terms of this work. Virginie Papadopoulou. 5 6 Abstract This work focuses on developing new techniques towards the quantification of hyperbaric decompression stress. Instead of just preventing decompression sickness (DCS), the aim is to go towards developing an environmental cardiovascular personalised stress index, especially as sub-clinical long terms effects of even recreational scuba diving have been demonstrated. From an engineering perspective, despite the longevity of the research field, a number of fundamental issues that remain unknown have prevented efficient modelling. The aim of this thesis is to directly tackle the research methodology by developing three tailored tools. Firstly, we develop a simulation platform in MatLab to model the diving process by optimizing the implementation of dissolved gas phase tracking decompression algorithms. This platform can be used to simulate diving scenarios, but also analyse real dive profiles. From a first analysis on real profiles provided to us by the European Divers Alert Network database, we find as expected that these existing models are poor predictors of accidents, but also demonstrate that ascent rate seems to be an important predictor of DCS for the range of profiles considered. Secondly, a fundamental issue for modelling the decompression phenomenon is that the precise formation site and growth mechanism of decompression bubbles in vivo remains unknown. We develop a novel experimental set-up and analysis code for the real-time optical study of decompression induced bubble growth dynamics. Looking at bubble growth from a gas saturated solution on ex-vivo muscle and fat tissues, we show that the role of the substrate from which bubble detach plays a significant role. Bubble density, nucleation threshold, detachment size and coalescence behaviour are shown significantly different for the two substrates, whereas growth rates after a critical size are governed by diffusion as expected, and a competition for dissolved gas between adjacent multiple bubbles is demonstrated. These findings are not accounted for in current modelling efforts so our experimental set-up could be used in the development of a more physiologically relevant decompression model. Thirdly, an important question in terms of decompression modelling optimisation is the precise definition of the evaluation endpoint. Vascular circulating bubbles are normally assessed semi-quantitatively by trained human raters who grade the severity on echocardiograms. We show statistically that this is highly rater-dependent compared to a new 7 counting methodology which is found to perform significantly better but is more time- consuming. We then use image processing techniques to semi-automate this new counting methodology with good comparison to human raters, significantly reducing the time needed for the assessment. This new method could be added to decompression model validation protocols, as well as used in physiology experiments looking at predictive parameters for, or preventive measures against, circulating gas bubbles post-dive. The proposed experimental and computational techniques could be used towards optimising the cardiovascular risk assessment of hyperbaric decompression stress caused by circulatory bubble dynamics. 8 Acknowledgements “From birth, man carries the weight of gravity on his shoulders. He is bolted to earth. But man has only to sink beneath the surface and he is free” —Jacques Yves Cousteau, Oceanographer and Explorer When I first started looking into decompression modelling for scuba diving as a one year research project, little did I know I would end up embarking on a PhD. As a physicist and keen scuba diver myself, I had become fascinated by understanding why modelling efforts had not been more successful in the past. On starting this project, I quickly realised the physiology associated to scuba diving was much more than just pressure effects and encompassed cold exposure, exercise, immersion, stress, etc. Within months of diving into the literature and starting my own research I grew increasingly convinced I would want to do this for a longer period of time and started searching how to fund a full PhD. Now after three years on this topic, I believe I have made some small new contributions to the field that will prove useful in the endeavour towards the quantification of a personalised stress index associated to scuba diving. The last three years have been for me a learning journey on many levels, with a new passion discovered and incredible opportunities along the way. I should thank my diving instructor George Filios for conveying his passion for the sport with me as a child and training me all the way into the first levels of professional recreational diving. Interestingly, he intuitively implements a lot of the physiological advice we are only now discovering through scientific studies. Without him, I would never have discovered this field. I am eternally grateful to both Dr Mengxing Tang and Dr Robert Eckersley who welcomed me into their research group when they really had no reason to. I have learnt a great deal about contrast enhanced ultrasound imaging in the process. I am also indebted to Prof. Costantino Balestra and Prof. Thodoris Karapantsios for agreeing to co-supervise and add their expertise to this collaborative endeavour. I am much obliged to Prof. Balestra for funding me through the PHYPODE project when a position became available in his team and 9 giving me an invaluable perspective of human research and physiology training. I am also very thankful to Prof. Karapantsios for numerous in depth discussions and for hosting me for three months in his laboratory to perform experiments on bubble growth. I could not have dreamed of a better supervisory team and I am extremely lucky that they were not only excellent supervisors but also excellent mentors. It is interesting how all four were complementary in the training and scientific advice they offered me through their expertise in different fields, yet invariably consistent both in reinforcing good scientific practice and in their mentoring advice. I would like to thank our Ultrasound Imaging Group at Imperial College London and King’s College London, including the students I supervised during my PhD, in particular Joe How Hui and Chris Song, as well as Prof. David Cosgrove, Dr James Choi, Dr Jennifer Siggers, Prof. Kim Parker and Prof. Eleanor Stride for useful feedback. Thank you to all the PHYPODE supervisors and fellows for brainstorming future directions in the field and to the Multiphase Dynamics Group of the Aristotle University of Thessaloniki for their warm welcome and hosting. Finally, I am very lucky to have had the chance to interact with other established researchers in the community who have all been very approachable and thank in particular Dr Thodoris Mesimeris, Dr Adel Taher, Mr Jean-Pierre Imbert, Prof. Saul Goldman, Prof. Radek Pudil, Dr Antoine Boutros, Dr Ole Hyldegaard, Dr Andreas Mollerlokken, Prof. David Doolette and Prof. Ran Arieli. I am also very grateful to DAN Europe and in particular Massimo Pieri, Danilo Cialoni and Dr Alessandro Marroni for generously giving us access to their diving research database used in Chapter 3.2. With respect to Chapter 4, I owe to acknowledge in more detail the help of: Dr Sotiris Evgenidis in setting up the experimental unit; Christos Ampatzidis for pH and conductivity analyses; Rania Oikonomidou for helping out in the last experiments; Dr Kelly Pavlidou and Prof. Ioannis Savvas of the Veterinary School for the handling of the animals and tissue conservation advice; Dr Mesimeris for generously providing his small pressure chamber to modify and use; and Dr Thodoris Mesimeris and Prof. Margaritis Kostoglou for fruitful discussions on the direction of this work. I would also like to thank Dr Peter Germonpré, Dr George Obeid, as well as Walter Hemelryck for their help for Chapter 5: they were instrumental in explaining the technique 10 and challenges of acquiring echocardiography post-dive in the field before I had the chance to participate in field experiments. Without their previous work in optimising this process from an acquisition point-of-view, much of the quantification effort presented here would not have been nearly as successful. Furthermore, I should acknowledge the funding sources that have supported the research
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