Simultaneous denoising and registration for accurate cardiac diffusion tensor reconstruction from MRI

Abstract

Cardiac diffusion tensor MR imaging (DT-MRI) allows to analyze 3D fiber organization of the myocardium which may enhance the understanding of, for example, cardiac remodeling in conditions such as ventricular hypertrophy. Diffusion-weighted MRI (DW-MRI) denoising methods rely on accurate spatial alignment of all acquired DW images. However, due to cardiac and respiratory motion, cardiac DT-MRI suffers from low signal-to-noise ratio (SNR) and large spatial transformations, which result in unusable DT reconstructions. The method proposed in this paper is based on a novel registration-guided denoising algorithm, that explicitly avoids intensity averaging in misaligned regions of the images by imposing a sparsity-inducing norm between corresponding image edges. We compared our method with consecutive registration and denoising of DW images on a high quality ex vivo canine dataset. The results show that the proposed method improves DT field reconstruction quality, which yields more accurate measures of fiber helix angle distribution and fractional anisotropy coefficients.