| Summary: | Diffusion magnetic resonance imaging (MRI) is a technique that allows us to probe the microstructure of materials. The standard technique in diffusion MRI is diffusion tensor imaging (DTI). However, DTI can only model a single fibre orientation and fails in regions of complex microstructure. Multiple-fibre algorithms aim to overcome this limitation of DTI, but there remain many questions about which multiple-fibre algorithms are most promising and how best to exploit them in tractography. This work focuses on exploring the potential of multiple-fibre reconstructions and preparing them for transfer to the clinical arena. We provide a standardised framework for comparing multiple-fibre algorithms and use it for a robust comparison of standard algorithms, such as persistent angular structure (PAS) MRI, spherical deconvolution (SD), maximum entropy SD (MESD), constrained SD (CSD) and QBall. An output of this framework is the parameter settings of the algorithms that maximise the consistency of reconstructions. We show that non-linear algorithms, and CSD in particular, provide the most consistent reconstructions. Next, we investigate features of the reconstructions that can be exploited to improve tractography. We show that the peak shapes of multiple-fibre reconstructions can be used to predict anisotropy in the uncertainty of fibre-orientation estimates. We design an experiment that exploits this information in the probabilistic index of connectivity (PICo) tractography algorithm. We then compare PICo tractography results using information about peak shape and sharpness to estimate uncertainty with PICo results using only the peak sharpness to estimate uncertainty and show structured differences. The final contribution of this work is a robust algorithm for calibrating PICo that overcomes some of the limitations of the original algorithm. We finish with some early exploratory work that aims to estimate the distribution of fibre-orientations in a voxel using features of the reconstruction.
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