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Statistical characterisation of brain tissue microstructure with diffusion magnetic resonance imaging


Project Description

Diffusion Magnetic Resonance Imaging (dMRI) is a medical imaging technique that allows to characterise brain structures in the microscale in vivo and non-invasively. The capabilities of dMRI to describe tissue at the cellular level depend on signal models providing relevant tissue parameters from MR acquisitions. Most of the existing models assume simplified tissue characteristics (e.g. spherical/cylindrical cells), imposing a hard constraint to the validity of the results and the knowledge that can be extracted from them. Moreover, these signal representations do not take into account the statistical variability existing within a measuring unit (~1mm), limiting the scopes of the technique.

In this project, the student will introduce a novel representation of tissue based on statistical descriptors. This approach has been used for a long time in engineering for describing microstructure of materials but never applied to brain tissue. This framework has the advantage of capturing the relevant moments for characterising tissue from a statistical perspective, and to evaluate changes due to degeneration.
The research plan has four stages to be completed in 3.5 years: 1. Literature search, training in brain tissue characteristics and diffusion MRI (yr 0.0-0.7). 2. Analysis of statistical descriptors for representing brain tissue (yr 0.7-1.5). 3. Study of the relation between statistical descriptors and the corresponding dMRI measurements (yr 1.5-2.5). 4. Practical demonstrations, reporting, and viva (yr 2.5-3.5). Those with an existing background in dMRI will be able to move into stage 2 sooner. Experiments will be performed numerically and in the Connectom scanner, a unique facility available in CUBRIC for characterising tissue microstructure based on diffusion experiments.

This project is particularly suited to students with an interest in MR-physics, statistical modelling, and numerical analysis with neuroimaging applications. The candidate is expected to be knowledgeable in Matlab/Python and to hold a degree in Physics, Engineering, or similar.

Funding Notes

Full UK/EU tuition fees plus stipend matching UKRI Minimum.

Full awards are open to UK Nationals and EU students who meet UK residency requirements. To be eligible for the full award, EU Nationals must have been in the UK for at least three years prior to the start of the course including for full-time education.

A small number of awards may also be made available to EU Nationals who do not meet the above residency requirement, provided they have been ordinarily resident in the EU for at least three years before the start of their proposed programme of study.

References

L Beltrachini, ZA Taylor, AF Frangi, “A parametric finite element solution of the generalised Bloch–Torrey equation for arbitrary domains”, Journal of Magnetic Resonance 259, 126-134 (2015).

M Mozumder, L Beltrachini, Q Collier, JM Pozo, AF Frangi, “Simultaneous magnetic resonance diffusion and pseudo‐diffusion tensor imaging”, Magnetic resonance in medicine 79 (4), 2367-2378 (2018).

L Beltrachini, N Von Ellenrieder, CH Muravchik, “Error bounds in diffusion tensor estimation using multiple-coil acquisition systems”, Magnetic resonance imaging 31 (8), 1372-1383 (2013).

How good is research at Cardiff University in Physics?

FTE Category A staff submitted: 19.50

Research output data provided by the Research Excellence Framework (REF)

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