Autor: |
Young, LAJ |
Přispěvatelé: |
Brady, J, Francis, S, Rodgers, C, Friend, P, Coussios, C |
Jazyk: |
angličtina |
Rok vydání: |
2022 |
Předmět: |
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Popis: |
Liver transplantation is the only definitive treatment for end-stage liver disease. However, a shortage of suitable donor organs means that currently up to 30% of patients become too ill or die before ever receiving a transplant. Normothermic machine perfusion (NMP) offers a potential solution by reducing preservation injury which might enable the safe transplantation of higher-risk livers, thereby expanding the donor pool and reducing waiting list mortality. To achieve the full potential of machine perfusion in transplantation, a set of sensitive and specific biomarkers are needed to enable accurate prediction of a donated livers ability to survive the transplantation process and function in the recipient. Unfortunately, such a set of biomarkers is not yet established. This thesis explores the potential of magnetic resonance (MR) imaging and spectroscopy to provide novel biomarkers for the prediction of transplantability in ex vivo livers. Deceased donor transplantation necessarily subjects livers to a metabolic and physiological insult during death of the donor. I quantified this using magnetic resonance in porcine models. Then I assessed the ability of temperature-corrected MR techniques to assess organ viability in ex vivo human liver during the traditional preservation of static cold storage. I then demonstrated non-invasive assessment of porcine livers during prolonged perfusion on a novel MR-compatible normothermic machine perfusion (NMP) system. Finally, I showed the proof-of-principle for performing MR assessment of ex vivo human livers during NMP. Transplantation is currently undergoing a dramatic transformation with the incorporation of machine perfusion technologies and this thesis suggests that MR techniques hold immense potential for providing non-invasive assessment of transplantability in ex vivo livers. |
Databáze: |
OpenAIRE |
Externí odkaz: |
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