Mathematical and computational models for diffusion magnetic resonance imaging (dMRI)

Overview

https://research.qut.edu.au/cbt/https://research.qut.edu.au/cbt/

In 1985, the first image of water diffusion in the living human brain came to life. Since then significant developments have been made and diffusion magnetic resonance imaging (dMRI) has become a pillar of modern neuroimaging.

Over the last decade, combining computational modelling and diffusion MRI has enabled researchers to link millimetre scale diffusion MRI measures with microscale tissue properties, to infer microstructure information, such as diffusion anisotropy in white matter, axon diameters, axon density, intra/extra-cellular volume fractions, and fibre orientation distribution. This microstructure information can provide significant insights on the diagnosis, progress and treatment of diseases, such as stroke, tumour, Alzheimer’s disease, and multiple sclerosis.

However, conventional modelling frameworks were developed assuming Gaussian diffusion of water molecules in the living tissue, which violates what is observed in the diffusion MRI signals. Hence, new modelling frameworks and computational methods that can capture non-Gaussian diffusion are needed urgently [1,2,3].

In this research field, we focus on developing new mathematical and computational models for diffusion MRI to advance the field of microstructure imaging in the brain. Research will be conducted with both domestic and international collaborators.

References

1. Q. Yang, D.C. Reutens, V. Vegh. (2022) Generalisation of continuous time random walk to anomalous diffusion MRI models with an age-related evaluation of human corpus callosum. NeuroImage (IF=6.556, ranking 1/14 in the neuroimaging category). https://doi.org/10.1016/j.neuroimage.2022.118903
2. V. Vegh, S Moinian, Q. Yang, D.C. Reutens. (2021) Fractional order magnetic resonance fingerprinting in the human cerebral cortex. Mathematics 9 (13), 1549.
3. Q. Yang, S. Puttick, Z. Bruce, B.W. Day, V. Vegh. (2020) Investigation of Changes in Anomalous Diffusion Parameters in a Mouse Model of Brain Tumour. Computational Diffusion MRI., pp. 161-172.

Research activities

In this project, depending on the study level, your research activities can be tailored to include some of the following (but not limited to):

• studying the mathematical models (including both Gaussian and non-Gaussian diffusion models) that underpin the diffusion MRI signals
• analysing diffusion MRI data of human brain
• developing numerical simulation of diffusion MRI signals based on realistic brain tissue microstructure
• performing non-linear least squared parameter fitting of data and proposed models
• generating parameter maps that provide specific brain tissue properties, such as diffusivity, axon diameter, neurite orientation and density,  intra- and extra- cellular volume fractions.

Outcomes

At the end of the project, you will:

• write a report/thesis to document the research activities and findings, which will contribute to at least one high-impact journal publication
• present your findings to peer students and academics at research seminars/conferences.

Skills and experience

We are looking for highly motivated students who are passionate about learning and research.

You should be familiar with:

• differential equations
• MATLAB.

No prior knowledge on diffusion magnetic resonance imaging or brain tissue microstructure is required.

Scholarships

You may be eligible to apply for a research scholarship.

Explore our research scholarships

Keywords

• computational modelling of diffusion MRI
• anomalous diffusion models
• microstructure imaging
• numerical simulations

Contact

Contact the supervisor for more information.