Developments in 2D NMR relaxometry and its application to biological tissue
Abstract In this thesis the capability of 2D NMR relaxometry to distinguish between different biological tissues is established using fresh unpreserved samples of lamb’s liver and kidney. A novel use of 2D T1-T2 relaxation spectra to provide characteristic profiles of specific tissues in specific states of health is proposed and tested in the case of osteoarthritis using human articular knee cartilage obtained from the Norfolk and Norwich University Hospital (NNUH). It is then proposed that 2D relaxation spectra can be used to optimise image contrast, which is an outstanding problem in clinical MRI. Indeed clinical MRI lacks well established and accurate methods for optimising image contrast and fails to exploit much of the potential available to the NMR practitioner. In this thesis two methods for the optimisation of image contrast using 2D T1-T2 relaxation spectra are proposed and tested. These are named the Virtual Sample Simulation (VSS) and MRI COntrast Modelling (MRICOM) methods. It is shown that MRICOM is more generally applicable because it exploits the established Object-oriented Development Interface for NMR (ODIN). It is demonstrated that ‘in-silico’ methods can predict image intensity of specific tissues using specific imaging sequences and use them to optimise contrast between tissues. A newly developed single shot T1-T2 sequence named the ‘TR method’ is proposed and implemented in order to increase the speed of 2D NMR relaxometry by between 2 and 10 times. Its ability to distinguish between different biological tissues is established, again using fresh unpreserved samples of lamb’s liver and kidney. Future work is then proposed to combine this faster method with other time reduction methods and volume selective techniques to create the CURE (Clinical Ultrafast RElaxometry) protocol. Methods are also proposed to increase the tissue characterisation and diagnostic capabilities of 2D NMR relaxometry with the use of expert systems and neural networks.
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