Enhancement of Multiple Fibre Orientation Reconstruction in Diffusion Tensor Imaging by Single Channel ICA
- When?
- Monday 11 July 2011, 14:00 to 15:00
- Where?
- 39BB02
- Open to:
- Staff, Students
- Speaker:
- Dr Min Jing, University of Ulster
To date, diffusion tensor imaging (DTI) is the only non-invasive tool available to reveal the neural architecture of human brain white matter. Advances in DTI techniques have shown great potential in the study of brain white matter related diseases such as depression, traumatic brain injury and Alzheimer's disease (AD). In DTI, a reliable reconstruction of neural fibre structure relies on the accurate estimation of fibre orientation distribution function (fODF) from each individual voxel in diffusion weighted images (DWI).
Recently developed high angular resolution diffusion imaging (HARDI) has overcome the limitations in early proposed DTI model, which can reveal the complex fibre structure such as multiple fibre crossing within a voxel. However, the HARDI based methods usually require relatively high b-values and a large number of gradient directions to reach good results. Such requirements are not always easy to meet in common clinical studies due to limitation in MRI facility. Moreover, most of these approaches are sensitive to noise. In this study, we propose a new framework to enhance the performance of the spherical deconvolution (SD) approach in low angular resolution DWI by employing a single channel blind source separation (BSS) technique to decompose the fODF initially estimated by SD such that the desired fODF can be extracted from the noisy background. The results based on numerical simulations and phantom data demonstrate that the proposed method achieves better performance than SD in terms of robustness to noise and variation in b-values. In addition, the results from in vivo data have shown that the proposed method has the potential to be applied to low angular resolution DWI which is commonly used in clinical studies.
Dr Min Jing currently works as a Research Associate in the Computational Neuroscience research team, Intelligent Systems Research Centre, University of Ulster. She received her MSc degree in digital signal processing with distinction from King’s College London in 2004. She then joined the
Centre of Digital Signal Processing in Cardiff University and received the doctorate in biomedical signal processing in 2008. Her PhD research was focused on the investigation of predictability of epileptic seizure by fusion of EEG and fMRI. Her current research is focused on the development and application of advanced digital signal processing techniques in diffusion tensor imaging.