Locally-adaptive Bayesian modelling for medical image reconstruction

The use of medical imaging techniques are critical in the early diagnosis and treatment of many serious conditions. Over the past 20-30 years there have been major advances in imaging speed and resolution along with equally dramatic decreases in cost. This means that every major hospital has access to highly sophisticated equipment. Although image reconstruction, an inverse problem, can be described as a statistical question, very few proposed methods have found their way into clinical practice. For example, the first paper recommending a Bayesian approach appeared more than 30 years ago, but the most widely used methods in the clinic are from more than 40 years ago. Even methods currently being developed, motivated by machine learning, are slow to progress beyond academic exercises because of practical drawbacks. In all of these cases, a critical issue is how to balance information from data with prior information in an automatic way which is robust to mismatches between prior model assumptions and reality. This project will consider a range of Bayesian modelling situations from simple Markov random field priors for SPECT and PET data, to hybrid kernel methods of combined PET/MR or PET/CT data. The automatic estimation of unknown prior parameters alongside image reconstruction will be investigated using a hierarchical Bayesian modelling approach. Similarly, extension to non-homogeneous models will allow locally adaptive methods. The most important stage will be to incorporate models for mismatch between prior specification and reality. Each of these cases has the potential to produce methods of practical importance and hence the project can have a major impact. Through the project supervisors, the student will have access to phantom and real data set covering a wide variety of medical applications and data collection techniques, and also to collaborators with significant practical experience.

<p>The earliest start date for this project is 1 October 2020.</p>

<p>Formal applications for research degree study should be made online through the&nbsp;<a href="https://www.leeds.ac.uk/info/130206/applying/91/applying_for_research_degrees">University&#39;s website</a>. Please state clearly in the research information section&nbsp;that the research degree you wish to be considered for is &lsquo;Locally-adaptive Bayesian modelling for medical image reconstruction&rsquo; as well as&nbsp;<a href="https://physicalsciences.leeds.ac.uk/staff/2/dr-robert-g-aykroyd">Dr Robert G Aykroyd</a>&nbsp;as your proposed supervisor.</p> <p>If English is not your first language, you must provide evidence that you meet the University&#39;s minimum English language requirements (below).</p> <p><em>We welcome applications from all suitably-qualified candidates, but UK black and minority ethnic (BME) researchers are currently under-represented in our Postgraduate Research community, and we would therefore particularly encourage applications from UK BME candidates. All scholarships will be awarded on the basis of merit.</em></p>

Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. The criteria for entry for some research degrees may be higher, for example, several faculties, also require a Masters degree. Applicants are advised to check with the relevant School prior to making an application. Applicants who are uncertain about the requirements for a particular research degree are advised to contact the School or Graduate School prior to making an application.

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid. Some schools and faculties have a higher requirement.

<p><strong>Self-Funding Students</strong></p> <p><strong>Funding Eligibility</strong></p> <p><strong>UK/EU</strong> &ndash;&nbsp;Leeds Doctoral Scholarship Award paying Academic Fees and Maintenance matching EPSRC rate of &pound;15,009 per year for 3 years, School of Mathematics Scholarship award paying Academic Fees and Maintenance matching EPSRC rate of &pound;15,009 per year for 3 years.&nbsp; Alumni Bursary is available to previous University of Leeds graduates offering 10% discount on Academic Fees.</p> <p><strong>International Students</strong> &ndash;&nbsp;China Scholarship Council-University of Leeds Scholarship Award paying Academic Fees for 3 years,&nbsp;School of Mathematics Scholarship award paying Academic Fees for 3 years, Commonwealth Scholarship and Commonwealth Split Site Scholarships.&nbsp; Alumni Bursary is available to previous University of Leeds graduates offering 10% discount on Academic Fees.</p>

<p>For further information regarding your application, please contact Doctoral College Admissions by&nbsp;email:&nbsp;<a href="mailto:EMAIL@leeds.ac.uk">m</a><a href="mailto:maps.pgr.admissions@leeds.ac.uk">aps.pgr.admissions@leeds.ac.uk</a>, or by telephone: +44 (0)113 343 5057</p> <p>For further information regarding the project, please contact Dr Robert Aykroyd by email:&nbsp;&nbsp;<a href="mailto:R.G.Aykroyd@leeds.ac.uk">R.G.Aykroyd@leeds.ac.uk</a></p>