Generation of IVIM parametric images using a kernelized total difference–based method

Quantitative analysis of Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) has been explored for many clinical applications since its development. In particular, the Intravoxel Incoherent Motion (IVIM) model for DW-MRI has been commonly utilized in various organs. However, due to the presence of excessive noise, the IVIM parametric images obtained from a pixel-wise biexponential fitting are often over-estimated and unreliable. In this study, we propose a kernelized total difference-based curve-fitting method to estimate the IVIM parameters. Both simulated and real DW-MRI data were used to evaluate the performance of the proposed method, and the results were compared with those obtained by two existing methods: Trust‐Region Reflective (TRR) algorithm and Bayesian Probability (BP). Our simulation results showed that the proposed method outperformed both the TRR and BP methods in terms of root-mean-square error. Moreover, the proposed method could preserve small details in the estimated IVIM parametric images. The experimental results showed that compared to the TRR method, both the proposed method and the BP method could reduce the over-estimation of the pseudo-diffusion coefficient and improve the quality of IVIM parametric images. The kernelized total difference-based curve-fitting method has the potential to improve the reliability of IVIM parametric imaging.