Novel Multi-Tensor Estimation for High-Resolution Diffusion Tensor Magnetic Resonance Imaging

The estimation of diffusion tensors in diffusion tensor imaging (DTI) is based on the assumption that each voxel is homogeneous and can be represented by a single tensor. As a result, estimation errors arise particularly in voxels with partial voluming of white matter or gray matter with cerebrospinal fluid (CSF) and voxels where fibers cross. Several authors have explored the possibility of solving for multiple tensors. Several authors analyzed the problem from the point of view of the number of unknowns and concluded that the solution is rather difficult due to the large number of unknowns and the nonlinearity of the equations. However, their approach was only helpful in eliminating some of the sources of artifacts in the DTI data but offered only a qualitative description of the model components. We implemented three strategies gradient, differentiation and exhaustive algorithms to solve and compare between their estimations at various conditions of signal to noise ratios (SNR)