Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) can track the brain nerve fiber and reconstruct non-invasively the three-dimensional image by tracing the local tensor orientation. The commonly used tracking method is usually based on the local diffusion information and insufficient
to consider the geometrical structure and fractional anisotropy which is constrained by anatomical structure and physiological function of human. Therefore, a novel brain nerve fiber tracking algorithm based on Bayesian optical-flow constrained framework is proposed. The construction of energy
function is the core step of global optical flow field estimation technology. In this paper, data fidelity constraint, prior constraint, penalty function and weight factor are introduced to construct Bayesian constraint function. The fiber trend model is displayed intuitively to obtain the
structure and direction of the inner nerve fibers of the brain, which can better assist in the diagnosis and treatment of clinical brain diseases, and lay a foundation for subsequent brain tissue research.
A Monte Carlo-Based Fiber Tracking Algorithm using Diffusion Tensor MRI