Introduction

Cardiac diffusion tensor imaging (CDTI) is a powerful tool that realizes the non-invasive detection of myocardial microstructure.¹ Several indices extracted from CDTI have shown the potential to identify microstructural abnormalities, including fractional anisotropy (FA), mean diffusivity (MD), helix angle (HA), and absolute secondary vector angulation (E2A).^2-3 However, the inherently low signal-to-noise ratio (SNR) of CDTI causes inaccurate estimation of these parameters. Several denoising methods have been proposed for brain DTI, based on the assumption of Rician distribution or the multi-directionality of DW images, such as adaptive nonlocal mean (ANLM), ⁴ local principal component analysis (LPCA), ⁵ Marchenko–Pastur principle component analysis (MPPCA). ⁶ The above methods can also be applied for CDTI, but their performance is unknown. In this study, we evaluate the performance of the above three algorithms in CDTI in simulation and ex-vivo experiments by the SNR of the image and the accuracy of the four parameters (FA, MD, HA, and E2A) after denoising.

Methods

In the simulation, we use the dataset downloaded from the IEEE dataport, including CDTI images and diffusion tensors (D). ⁷ The simulated reference image (S0) is generated by setting the values in the myocardium to 1000 and the background to 0. Then diffusion images () are simulated according to the Eq. (1):
$$S_{DWI}=S0\ast e^{-b\ast g^{T}\ast D\ast g}$$ (1)
where g is the gradient vectors, b is the b-value = 350 s/mm2. Then white Gaussian noise is added to the simulated CDTI images with SNR = 5, 10, 15, 20, and 25. The SNRs of the denoised images using ANLM, LPCA, and MPPCA are calculated. CDTI parameters are computed from the noise-free and denoised images, respectively, using a home-made software. The root-mean-square error (RMSE) between them evaluates the parameter estimation accuracy.
In the ex-vivo experiment, the CDTI data of 37 slices of the failing heart replaced from four patients are acquired on a 3T scanner (Ingenia, Philips, The Netherlands). The study is approved by the Medical Research Ethics Committee (No.KY20200390101) of Guangdong Provincial People's Hospital. Imaging parameters are turbo spin-echo DTI sequence with 16 diffusion encoding directions, b-value=800 s/mm2, repetition = 8. The average images of 8 repetitions with LPCA denoising and the corresponding indices are used as the gold standard. The average images with repetition = 1, 2, 4, 6, 8 are used to imitate images with different SNR levels. The three methods are then applied on average images. The SNRs of denoised images are calculated as the mean signal intensity in the myocardium divided by the standard deviation in the background. CDTI parameter maps are computed from denoised images and the gold standard, then RMSE between them indicates accuracy.

Results

In the simulation, ANLM and LPCA produce clear images, while MPPCA can hardly reduce noise (Fig. 1). Evident inhomogeneity can be seen in the noisy map, then the parameters get more accurate after denoising from the LPCA and ANLM (Fig 2). In the ex-vivo experiment, the improvement of LPCA is obvious, and the SNR is higher than the other two methods. MPPCA performs worst as the residual noise exists obviously (Fig. 3). As the SNR increases, the RMSE of all parameters and algorithms shows a downward trend (Fig. 4, 5). Combined with visual judgment, LPCA can achieve apparent homogeneity, and consistent with the previous study, ⁵ the most significant reduction in uncertainty of parameter estimates is obtained using the LPCA method than another method.

Discussion and conclusion

The three post-processing methods improve image quality and accuracy in parameter estimates in the ex-vivo experiment. The ANLM performs well in low SNR but seems to over smooth some image details. The MPPCA removes only thermal noise, resulting in apparent noise residue. The LPCA reduces the noise and preserves image contrast, and produces accurate parameters that reflect the tissue's characteristics. We suggest using LPCA to improve image quality and parameter estimations accuracy in CDTI imaging.

Acknowledgements

This work is supported in part by the National Natural Science Foundation of China under grant nos. 61771463,81971611, National Key R&D Program of China nos. 2020YFA0712202, 2017YFC0108802 , the Innovation and Technology Commission of the government of Hong Kong SAR under grant no. MRP/001/18X, and the Chinese Academy of Sciences program under grant no. 2020GZL006..