Available projects for MSc-MIC students 2011-2012

Theme: Physics; Computing

Description: This project aims to develop a super-resolution technique for diffusion MRI. Diffusion MRI is a magnetic resonance imaging technique at the very foundation of recent advancement in imaging microstructure of tissues non-invasively. The technique enables us to understand the structural basis of normal and abnormal brain functions in living human beings. However, one limitation is that the images acquired with this technique are limited in its spatial resolution, typically of 2x2x2 mm^3 in voxel size. Compared to the typical T1-weighted images, which have voxels of size 1x1x1 mm^3, the resolution is 8 times lower, preventing the imaging of fine brain structures. Improving spatial resolution is essential for accessing such finer detail. The challenge is that doing so by directly acquiring higher resolution data is currently infeasible, due to the excessively long scan time required. The alternative is super-resolution, which refers to a family of techniques that reconstruct a high resolution image from a small set of lower resolution ones. Because the set of lower resolution images can be acquired with modest increase in the scan time, super-resolution makes an attractive avenue to explore. The aim of this project is to develop such a technique that is applicable specifically to diffusion MRI.

Student: Alexandra Tobisch (Awarded Distinction, currently pursuing her PhD at German Centre for Neurodegenerative Diseases)

Theme: Physics; Computing

Description: This project aims to develop a technique towards clinical MR imaging of brain microstructure. It is within the broader aim of the Microstructure Imaging Group at UCL to develop non-invasive imaging techniques for assisting disease diagnosis. The particular MR imaging modality that we use is known as the diffusion MRI. It acquires images, known as the diffusion-weighted images, that encode information about tissue microstructure. This is often precisely the information that doctors seek to diagnose a disease but require a biopsy to collect. However, biopsy procedures are painful and, more important, can often be prohibitive due to their significant risk to a subject. Accessing this information non-invasively opens the door for diagnosing diseases early before they cause irreversible damages to the patients.

However, there is a particular challenge for clinical applications of this technique: noise. MRI images are generally contaminated with noise and this is especially true for diffusion-weighted images. The presence of noise renders the information derived from MRI less accurate, potentially introducing systematic errors, known as the bias. Being able to quantify and remove this bias is critical for generating accurate information for follow-on analysis. The aim of this project is to develop such a technique for reducing noise-induced bias in tissue microstructure estimates. Specifically, we will investigate a modern statistical method known as SIMEX: the SIMulation and Extrapolation method, which provides an estimation of the bias by adding synthetic noise to the measured data. The project will evaluate the feasibility of SIMEX as a viable approach to derive more accurate tissue microstructure estimates from diffusion-weighted images.

Student: No suitable candidates