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

In this study, a fast and clinically feasible residual-bootstrapping algorithm using a geometrically constrained two-tensor diffusion model is employed for estimating uncertainty in fibre orientation. Voxels are classified based on tensor morphologies before applying a single or two tensor residual-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 and computational times for whole bootstrap data volume generation compared to other multi-fibre model techniques, facilitating widespread clinical use. Tractography with two-tensor residual-bootstrapping is also developed to estimate the connection probabilities between brain regions, especially regions with complex fibre configurations. Experimental results on a hardware phantom and in vivo data demonstrate the superior performance of our approach compared to conventional approaches.