Multi-component fiber track modelling of diffusion-weighted magnetic resonance imaging data

In conventional diffusion tensor imaging (dti) based on magnetic resonance data, each voxel is assumed to contain a single component having diffusion properties that can be fully represented by a single tensor. even though this assumption can be valid in some cases, the general case involves the mixing of components, resulting in significant deviation from the single tensor model. hence, a strategy that allows the decomposition of data based on a mixture model has the potential of enhancing the diagnostic value of dti. this project aims to work towards the development and experimental verification of a robust method for solving the problem of multi-component modelling of diffusion tensor imaging data. the new method demonstrates significant error reduction from the single-component model while maintaining practicality for clinical applications, obtaining more accurate fiber tracking results.