Establishment of a Standard Image Processing Pipeline for Chemical Exchange Saturation Transfer Imaging
ALLOCATED IN 2015-2016
Chemical Exchange Saturation Transfer (CEST) is a recent MRI technique in which exogenous or endogenous molecules containing exchangeable protons are selectively saturated and after transfer of this saturation, detected indirectly through the water signal with enhanced sensitivity. By looking specifically at the signal from exchangeable groups, the method has shown great potential in oncology, by being able to separate tumour tissue from oedema in brain neoplasms, or tumour regrowth from radiation necrosis. In addition, enhanced detection of hydroxyl groups from native glucose molecules has recently been demonstrated in cancerous tissue, highlighting them in a manner similar to the well-established FDG-PET.
Currently however, there are no clear guidelines when it comes to the post-processing of the signal coming from CEST experiments, and the optimal estimation of the ‘CEST signal’ remains an active area of interest.
Therefore, the subject of this Ph.D. thesis is the development of a rapid Bayesian-based image-processing tool, making use of the latest models existing of the CEST signal available, to ease translation of this technology in the clinical world, in collaboration with Olea Medical, a SME specialised in the development of software analysis packages to analyse clinical data.
From UCL, the student will be able to learn: *Physical and chemical bases of the CEST mechanism (nature of the signal, exchange regimes and detection, field strength dependency, …) *MRI techniques to detect the CEST signal (including continuous saturation, FLEX, Spin-Lock, CPMG, …) *Access to clinical studies, mostly based on oncological problems, acquired on at least 3 different clinical MRI scanners, and being part of a large group of scientists working jointly on this topic
From Olea Medical, the student will gather the following expertise: *Learn about programming within a dedicated clinical-ready environment *Basics of rapid Bayesian programming