Comparison of three denoising methods for cardiac diffusion tensor imaging

Cardiac diffusion tensor imaging (CDTI) provides unique information on the structure, organization, and integrity of the myocardium. However, the inherently low signal-to-noise ratio (SNR) limits the accuracy in parameter estimations. Recently, several post-processing algorithms have been proposed to improve image quality in brain DTI, but their performance is unknown in CDTI. This paper evaluates three different denoising algorithms (ANLM, LPCA, and MPPCA) in CDTI regarding image quality and accuracy of parameters with simulation and ex-vivo experiments. We add white Gaussian noise to the simulated DWI images and average ex-vivo data with various repetitions to imitate CDTI images with different SNRs. Then, denoising methods are applied, and the performance is evaluated by SNR and RMSE. The results show that the SNR is improved by 72.38%, 127.01%, 0.6% in simulation, and 327.38%, 474.56%, 173.19% in ex-vivo experiments by the three methods, respectively. The uncertainty of parameters is reduced by all three algorithms in the ex-vivo experiment.