Dr J He, Dr T Gagliardi, Dr Yazan Masannat, Prof D Lurie
No more applications being accepted
Competition Funded PhD Project (European/UK Students Only)
About the Project
This project aims to translate, optimise and extend quantitative oxygen extraction fraction mapping based on oxygen enriched air inspiration and magnetic resonance (MR) relaxometric imaging for human application. This underpinning imaging method will be pivotal in the non-invasive assessment of mitochondrial function and abnormal metabolism, fundamental in the understanding and treatment of cancer.
Imaging methods, sensitive to underlying disease processes, are highly desirable to more effectively improve human health, especially in the context of cancer diagnosis and monitoring of treatment. In contrast to the anatomical visualisation capability of conventional magnetic resonance imaging (MRI), advanced MRI methods allow essential physiological information, such as vascular function, tissue microstructure and metabolic function, to be encoded in the acquired images. Although the information derived from these new MRI methods is potentially valuable for the assessment of disease, their translation into clinical use is lagging behind fundamental research, because of the unique requirements imposed by clinical applications.
Oxygen is essential to the aerobic energy production, while anaerobic energy production is employed under insufficient oxygen supply. The anaerobic pathway is not only inefficient in terms of amount of energy produced, but also alters the pH, affecting normal immune function. Lactate dehydrogenase (LDH) overexpression is the central feature of tumour metabolism, with an associated hypoxia and elevated lactate production in the tumour core.
MRI is a promising powerful non-invasive method for assessing tissue oxygen level. Since deoxyhaemoglobin is paramagnetic, generating local magnetic field disturbance within tissues, both the rate of signal dissipation (R2*) and the rate of tissue re-magnetisation (R1*) increases with the concentration of deoxyhaemoglobin. However, these methods are qualitative in nature, with R1* measurement influenced by imperfections of the scanner hardware. Recently, a method based on the employment of inspiration of oxygen-enriched air in conjunction with the observation of R1* was successfully developed and demonstrated in an animal model, allowing quantitative assessment of the oxygen extraction fraction (1). However, R1* measurement is lengthy compared to R2* assessment and humans can only be exposed to oxygen-enriched air for a limited time, while several acceleration methods are available (namely Look-Locker, fast reordering and DESPOT1 methods) (2).
The project includes three work packages:
(1) Development and optimisation of R1* and R2* acquisition schemes to estimate sensitivity and specificity;
(2) Development and optimisation of data feature extraction algorithms for the effective combination of R1* and R2* data; and
(3) Implementation and validation of this new acquisition approach on our clinical MRI scanner.
We are seeking a highly-motivated student with a background in a quantitative discipline such as physics, mathematics, engineering or computing science to join our cancer imaging team. Instruction in MRI physics can be provided through an internationally-renowned taught MSc module. The student will join a rapidly-expanding multidisciplinary team to develop and translate novel MRI methods for clinical cancer applications.
Aberdeen has one of the largest single-site medical school campuses in Europe, providing internationally-renowned MSc training in Medical Physics and a highly-rated undergraduate medical degree programme (ranked No 1 in Scotland and No 3 in the UK). Located in the heart of the medical school campus, Aberdeen Biomedical Imaging Centre (ABIC) has a long tradition in medical imaging, and has the infrastructure for clinical imaging studies with sophisticated equipment. The multidisciplinary investigation team has extensive experience and a strong track record in the development and clinical application of novel MRI methods, including hardware development, image analysis and programming. The successful candidate will be guided by Dr Jiabao He on MRI method development, Dr Tanja Gagliardi on clinical imaging, Dr Yazan Masannat on breast cancer treatment, Professor David Lurie on magnetic resonance, as well as being supported by a group of highly-energetic fellow students within the imaging research programme.
At the end of the PhD programme, the student will have acquired transferable skills including numerical simulation, image data analysis, clinical study management and MRI scanner programming, essential for the next stage in their career development. The student will benefit from a set of internal training programmes, specially designed to support the success of scientists working in the clinical environment.
For informal discussion, please email the lead investigator of cancer imaging research, Dr Jiabao He ([Email Address Removed]).
(1) Beeman, S. C., Shui, Y.-B., Perez-Torres, C. J., Engelbach, J. A., Ackerman, J. J. H., & Garbow, J. R. (2016). O2 -sensitive MRI distinguishes brain tumor versus radiation necrosis in murine models. Magnetic Resonance in Medicine, 75(6), 2442–2447.
(2) Eldeniz, C., Finsterbusch, J., Lin, W., & An, H. (2016). TOWERS: T-One with Enhanced Robustness and Speed. Magnetic Resonance in Medicine, 76(1), 118–126.