Use this URL to cite or link to this record in EThOS:
Title: Molecular MRI using exogenous enzymatic sensors and endogenous chemical exchange contrast
Author: Taylor, Alexander John
ISNI:       0000 0004 6060 640X
Awarding Body: University of Nottingham
Current Institution: University of Nottingham
Date of Award: 2016
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Restricted access.
Access from Institution:
Molecular magnetic resonance imaging (MRI) methods have the potential to provide detailed information regarding cellular and molecular processes at small scales within the human body. Nuclear signals from chemical samples can be probed using specialised MRI techniques, to highlight molecular contrast from particular enzymes or metabolites. The aim of the work described in this thesis is to investigate both exogenous and endogenous contrast mechanisms using fluorine MRI and chemical exchange saturation transfer (CEST) respectively, in order to detect molecular changes in vitro. Initial theoretical work investigates the factors which affect fluorine MRI signals and provides a theoretical framework to determine the sensitivity of such experiments. A novel paramagnetic fluorine sensor to detect enzyme activity is then characterised using high field nuclear magnetic resonance (NMR), showing 60 to 70–fold increases in T1 relaxation values upon enzyme interaction. The effects on the fluorine lineshape from varying sample temperature and solvent were investigated. The possibility of imaging is demonstrated, but further investigations using the theoretical framework found pre–clinical implementation of the sensor is limited by the achievable experimental sensitivity. Efforts then focussed on CEST molecular methods, which are not limited by sensitivity. A protocol is developed to target amide protons in an in vitro cancer cell model, with parameters optimised following simulation of the expected contrast. Analysis of CEST results were aided through use of a support vector machine (SVM) to distinguish group differences between cancer cells and control samples. A linear classifier was found to be suitable to discriminate between samples.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: WN Radiology. Diagnostic imaging