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.
Most partial volume correction (PVC) methods are ROI-based, and assume uniform activity within each ROI. Here, we extended a PVC method, developed by Rousset et al (JNM, 1998) called geometric transfer matrix (GTM), to a voxel-based PVC approach called v-GTM which accounts non-uniform activity within each ROI. The v-GTM method was evaluated using simulated data (perfect co-registered MRIs). We investigated the influence of noise, the effect of compensating detector response during iterative reconstruction methods and the effect of non-uniform activity. For simulated data, noise did not affect the accuracy of v-GTM method seriously. When detector response compensation was applied in iterative reconstruction, both PVC methods did not improve the recovery values. In the non-uniform experiment, v-GTM had slightly better recovery values and less bias than those of GTM. Conclusion: v-GTM resulted better recovery values, and might be useful for PVC in small regions of interest.
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