Magnetic Resonance Imaging (MRI)

Magnetic resonance imaging (MRI) is an imaging technique used primarily in medical settings to produce high quality images of the inside of the human body. MRI is based on the principles of nuclear magnetic resonance (NMR). Its primary use is for disease diagnosis (brain disease, MS, spinal cords, etc.), increasing understanding of the causes and progression of diseases, and as an objective and rapid method of assessing the effect of treatments on the course of disease.

We collaborate with Clinical Neurology, SPMMRC, Academic Radiology, Brain and Body Centre, and School of Mathematical Sciences.

Level Sets for Image Segmentation

Level set methods for image segmentation rely on an evolving closed surface defined by a moving interface, the front, which expands from a point out into the image, fitting itself to the region it is released within. The method involves solving a set of Partial Differential Equations. We have published in BIOSIGNALS 2008 a novel method for brain segmentation using Level Sets. Comparison of ground truth to the well-known FAST (developed by the team at the University of Oxford) and our swarm + level set solution is shown below. Our method has advantages over those based on image voxel classification. The numerical nature of the level set method (level set equations are solved using the finite element method) allows for a wide variety of metrics of the surface to be calculated. The gradient across the interface can be calculated easily, from which we can calculate curvature or other geometric data if required.

3D Gabor Wavelets for Image Registration

We have published in Journal of Medical Image Analysis (Impact Factor 3.256) a novel metod for evaluating image registration algorithms based on 3D Gabor wavelets. A 3D Gabor wavelet is a sinusoidal wave modulated by a 3D Gaussian function and can be defined as:

Diffusion Tensor Imaging

Diffusion Tensor Imaging (DTI) uses a set of DW images to reconstruct, on a voxel by voxel basis, a tensor that characterizes the three-dimensional diffusion profile. The eigenvalues of the diffusion tensor provide diffusivities along the orientations defined by the respective eigenvectors. The direction of highest diffusivity has been found to coincide with the principal fibre orientation in regions with coherent fibres. Utilizing these orientations, reconstruction of some white matter tracts is feasible using a process known as tractography [4,5]. Despite its potential in clinical application, the single tensor DTI model cannot describe diffusion profiles that arise from non-coherent fibres coexisting in the same voxel.

We have published in JMRI (impact factor 2.67) a two-tensor model to resolve fibre crossings from conventional DTI datasets (32 diffusion sensitizing directions, b=1000 s/mm2). This exploits the planar diffusion profile in regions with fibre crossings [1] and applies a spatial regularisation scheme to reduce noise artefacts. The regularisation scheme was based on relaxation labeling and on a discrete set of basis directions. Orientational, anisotropy, and diffusivity information could be resolved in regions of two fibre crossings using full brain coverage scans acquired in less than 6 minutes.

We have published in SPIE Medical Imaging 2008 a novel method for estimating parameters of a multi-tensor model for DTI. The method uses Bayesian inference, see image below.

We have submitted for publication in ISMRM 2008 a paper describing a novel method for brain fibre tracking. The method uses Bayesian inference and Markov chain Monte Carlo (MCMC)

We have also submitted for publication in ISMRM 2008 a paper describing a novel method for brain fibre tracking.

Evolutionary Computation for RF Pulse Design

Currently images from a MR scanner needs to be 'enhanced' or 'corrected' before they can be effectively used. It would make more sense to improve the physical MR image reconstruction process in the first place. By incorporating specific optimisation criteria to the pulse design process, we aim to develop methods for rapid acquisition of high quality MR images.

RF pulses are an essential part of MRI since they are the device by which nuclear spins can be excited out of their lowest energy state, and be detected to produce MR images. Pulses designed by the current methods suffer from sensitivity to B1 inhomogeneity. Namely, the values of the magnetic fields are not the same throughout the sample. The problems with pulse design become much more significant in ultra-high fields, e.g., 7 T. At high-field, SAR (specific absorption ration), a measure of the power absorbed in tissue per unit mass, is limited and this creates some of the most difficult challenges for imaging. Field inhomogeneities occur because of difference in bulk magnetic susceptibility (BMS).

We are developing new pulse design methods combining MR physics, mathematical functions, and evolutionary computational algorithms. The methods will be tested through computer simulation as well as on the Philips 7 T scanner housed by the SPMMRC.

MRI Classification

Classification fr Brain Fibre Tracking

Diffusion Tensor Imaging (DTI) is a variation of MRI that may significantly improve our understanding of brain structure and neural connectivity. Water diffusion in the brain is highly affected by its cellular organization. Consequently, the measured DTI becomes highly anisotropic and oriented in areas of compact nerve fibre organization, providing an indirect way of identifying fibre tracts. Analyzing such images can lead to a better understanding of white matter diseases, such as multiple sclerosis, schizophrenia and dyslexia.