An Adaptive Denoising Method Dedicated to Cardiac MR-DTI

Diffusion tensor magnetic resonance imaging (MRDTI) is noise sensitive, and the noise can seriously affect the subsequent characteristic parameter calculations. This paper proposes an adaptive denoising method based on a sparse representation for cardiac diffusion weighted images in MR-DTI. The method first generates a dictionary from the cardiac diffusion weighted images and then a dictionary training algorithm is applied to adapt the dictionary so that it better fits the features of the observed image. The denoising is achieved by gradually approximating the underlying image using the atoms selected from the generated dictionary. The results on both simulated images and real DT-MRI images from ex-vivo and in-vivo human hearts show that the proposed denoising method performs well in preserving image fine features and contrast.