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Deep Probabilistic Models for resolution enhancement of diffusion magnetic resonance imaging


Project Description

Diffusion magnetic resonance imaging (dMRI) measures the direction and rate at
which water molecules diffuse in biological tissues, e.g. brain tissue. Scalar metrics
derived from dMRI are used to assess changes in the brain tissue due to different
diseases. Also, as diffusion occurs preferentially along the direction of white matter
fiber bundles in the brain, dMRI can be used to map structural connectivity,
providing unique information with many potential applications, including predicting
brain tumour prognosis and for neurosurgical planning.

Due to the limited scan time typically available in the clinical setting, a result of both
time/cost and patient comfort considerations, clinical dMRI protocols rarely allow for
dMRI acquisitions with sufficient spatial resolution to provide a detailed description
of the complex microanatomy of the brain tissue.

Obtaining such a level of detail requires expensive scanners, with enhanced
gradient hardware and long acquisition times, both of which are beyond the
resources typically available in routine clinical practice.

In this project, our objective is to develop novel machine learning models for
increasing the spatial resolution of diffusion images. We are particularly interested
on developing new approaches for deep probabilistic models on non-Euclidean
manifolds that can be applied to improve resolution on these type of images.
A successful outcome will achieve the dual purpose of pushing the state-of-the-art
in resolution, or reducing scan time for a given resolution.

The starting date for this position is October 2020.

The Supervisors:

The candidate will be supervised by Dr Mauricio A Álvarez and Dr Paul Armitage.
Dr Álvarez is a Senior Lecturer in Machine Learning at the Department of
Computer Science, University of Sheffield. Dr Álvarez is well known for his work on
fundamental research on probabilistic models in general, and in particular for
Gaussian Processes for Machine Learning. More information can be found in the
following link https://maalvarezl.github.io/
Dr Armitage has been working in the field of magnetic resonance imaging
acquisition and image analysis for over 20 years, with a particular interest in
quantitative diffusion imaging of the brain. He will work closely with Dr Álvarez and the candidate to ensure that the methods developed during the project are as useful as possible in the application domain.

The Departments:

The Department of Computer Science at the University of Sheffield was
established in 1982 and has since attained an international reputation for its
research and teaching. In the latest Research Excellence Framework (REF2014),
45% of the research in the department was recognised as internationally excellent
in terms of originality, significance and rigour, and another 47% as internationally
world leading. These results place the department among the top 5 UK Computer
Science departments for research excellence.

The Academic Unit of Radiology is an active research group within the Department
of Infection, Immunity and Cardiovascular Disease (IICD), focused on MR imaging.
The main research themes of the Unit are Neuroscience, Paediatric/Neonatal/In
Utero and Cardio/Pulmonary MRI. Research MRI scans are performed in all of
these fields and the Unit has two whole body research scanners (1.5T and 3T) at
the Royal Hallamshire Hospital and a further 1.5T scanner at the Northern General
Hospital to support this work.

Candidate Requirements:

-A first degree in Mathematics, Statistics, Physics, Computer Science or Engineering
-An MSc in Mathematics, Statistics, Physics, Computer Science or Engineering with
a 2.1 degree.

If English is not your first language, you must have an IELTS score of 6.5 overall, with no less than 6.0
in each component.

The candidate is expected to have solid mathematical background and strong
programming skills. Relevant experience and publications in the methods and/or
applications above are desirable. Please refer to the FAQ at
https://www.sheffield.ac.uk/postgraduate/phd/research

To apply for the studentship, applicants need to apply directly to the University of
Sheffield using the online application system. Please name Mauricio Alvarez as your
proposed supervisor.
Complete an application for admission to the standard Computer Science PhD
programme
http://www.sheffield.ac.uk/postgraduate/research/apply

Applications should include a research proposal, CV, transcripts and two references.
The research proposal (up to 4 A4 pages, including references) should outline your
reasons for applying for this scholarship and how you would approach the
researching, including details of your skills and experience in Machine Learning.

Funding Notes

This is a 3.5 years full scholarship for UK/EU applicants, covering tuition fees,
standard living costs and a Research Training Support Grant of £4,500 (across the
award). For other applicants, the same amount of scholarship is available but they
have to find additional financial support to cover the difference between home fee
and international fee.

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