PhD Studentship in Magnetic Resonance Imaging
University of Surrey - Department of Electrical & Electronic Engineering
|Funding for:||UK Students, EU Students|
|Placed on:||18th August 2016|
|Closes:||31st October 2016|
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Post Type: Full Time
Closing Date: Monday 31 October 2016
Quantitative Magnetic Resonance Imaging
A fully-funded PhD Studentship is available for a UK or EU student to join the Medical Imaging lab at the Centre for Vision, Speech and Signal Processing, University of Surrey. This studentship aims to use novel Magnetic Resonance (MR) Imaging methodologies to quantify structural properties related to cellularity and endogenous concentrations of metabolites. Such parameters are important markers of diseases and their quantification will further improve the diagnostic accuracy of MR.
MRI comprises a comprehensive variety of acquisition protocols, aiming to provide both functional and anatomical representations of the subject. However, unlike other imaging modalities, MR lacks standardized signal intensity, e.g. the Hounsfield units in computed tomography (CT) or the standardized uptake value in PET. Essentially, signal intensities depend on the scanner, sequence etc, making MR data qualitative rather than quantitative. In contrast, parameters that model the signal are in theory “quantitative” since they depend solely on the relative change in MR signal rather than its absolute value. In reality this is not the case as they are affected by field inhomogeneities, gradients, SNR of the reconstructed images, time resolution etc. Besides scanner limitations, “quantification” of such parameters also depends on how accurately the model describes the MR signal and how precise is their derivation. Currently, parameters that model the signal are derived from the reconstructed MR images using models that describe the signal evolution. This studentship will build on new exciting advancements that suggest an alternative approach, where the parameters that model the signal are the aim of reconstruction instead of the image.
The successful candidate will develop novel MR acquisition (on a Siemens 3T scanner) and reconstruction methodologies, where the parameters related to structural properties and metabolics will be reconstructed directly from the measured MR signal. The goal is to reduce measurement and/or estimation errors resulting in more accurate quantification. The quantified parameters will be also validated in terms of repeatability, and ability to detect cancer.
The Centre for Vision, Speech and Signal Processing (CVSSP) is one of the largest research centres in the UK focusing on signal processing and interpretation. Its aim is to advance the state of the art in multimedia signal processing and computer vision, with a focus on image, video and audio applications. CVSSP expertise and activities span Computer Vision, Digital Signal Processing, Machine Learning and Artificial Intelligence, Computer Graphics and Human Computer Interaction, Medical Image Analysis and Multimedia Communication.
Candidates should hold a 1st or 2:1 Bachelor’s degree and preferably a Masters’ degree with distinction in an appropriate discipline (e.g., engineering, computer science, signal processing, applied mathematics, physics). They should be able to demonstrate excellent mathematical, analytic, and programming skills.Preference will be given to applicants with experience in: MR imaging/spectroscopy, and machine learning.
Non-native speakers of English will normally be required to have IELTS 6.5 or above (or equivalent).
Funding is available for UK or EU nationals only and covers full tuition fees (home rate) and a stipend at the rate specified by the Research Council (rate for 2016-2017 is £14,057 p.a. tax-free). The award will be for a period of 3 years from the starting date.
How to Apply:send (i) cover letter, (ii) curriculum vitae, (iii) outline research proposal (max. 2 pages), (iv) details of two academic referees, and (v) copies of transcripts and certificates of qualification toDr Nikolaos DikaiosN.Dikaios@surrey.ac.uk. For specific information about the PhD research project please contact Dr Nikolaos Dikaios.
Suitable candidates will be shortlisted and invited to complete the formal application process including an interview.
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