Structure Prior Constrained Estimation of Human Cardiac Diffusion Tensors

Objective: The purpose of this paper is to increase the accuracy of human cardiac diffusion tensor (DT) estimation in diffusion magnetic resonance imaging (dMRI) with a few diffusion gradient directions. Methods: A structure prior constrained (SPC) method is proposed. The method consists in introducing two regularizers in the conventional nonlinear least squares estimator. The two regularizers penalize the dissimilarity between neighboring DTs and the difference between estimated and prior fiber orientations, respectively. A novel numerical solution is presented to ensure the positive definite estimation. Results: Experiments on ex vivo human cardiac data show that the SPC method is able to well estimate DTs at most voxels, and is superior to state-of-the-art methods in terms of the mean errors of principal eigenvector, second eigenvector, helix angle, transverse angle, fractional anisotropy, and mean diffusivity. Conclusion: The SPC method is a practical and reliable alternative to current denoising- or regularization-based methods for the estimation of human cardiac DT. Significance: The SPC method is able to accurately estimate human cardiac DTs in dMRI with a few diffusion gradient directions.