Model-Based Bootstrapping on Classified Tensor Morphologies using Constrained Two-Tensor Model

In this study, fast and clinically feasible model-based bootstrapping algorithms using a geometrically constrained two-tensor diffusion model are employed for estimating uncertainty in fibre-orientation. Voxels are classified based on tensor morphologies before applying single or two-tensor model-based bootstrapping algorithms. Classification of tensor morphologies allows the tensor morphology to be considered when selecting the most appropriate bootstrap procedure. A constrained two-tensor model approach can greatly reduce data acquisition times and computational time for whole bootstrap data volume generation compared to other multi-fibre model techniques, facilitating widespread clinical use. For comparison, we propose a new repetition-bootstrap algorithm based on classified voxels and the constrained two-tensor model. White matter tractography with these bootstrapping algorithms is also developed to estimate the connection probabilities between brain regions, especially regions with complex fibre configurations. Experimental results on a hardware phantom and human brain data demonstrate the superior performance of our algorithms compared to conventional approaches.