Image denoising substantially improves accuracy and precision of intravoxel incoherent motion parameter estimates

Abstract

Applicability of intravoxel incoherent motion (IVIM) imaging in the clinical setting is hampered by the limited reliability in particular of the perfusion-related parameter estimates. To alleviate this problem, various advanced postprocessing methods have been introduced. However, the underlying algorithms are not readily available and generally suffer from an increased computational burden. Contrary, several computationally fast image denoising methods have recently been proposed which are accessible online and may improve reliability of IVIM parameter estimates. The objective of the present work is to investigate the impact of image denoising on accuracy and precision of IVIM parameter estimates using comprehensive in-silico and in-vivo experiments. Image denoising is performed with four different algorithms that work on magnitude data: two algorithms which are based on nonlocal means (NLM) filtering, one algorithm that relies on local principal component analysis (LPCA) of the diffusion-weighted images, and another algorithms that exploits joint rank and edge constraints (JREC). Accuracy and precision of IVIM parameter estimates is investigated in an in-silico brain phantom and an in-vivo ground truth as a function of the signal-to-noise ratio for spatially homogenous and inhomogenous levels of Rician noise. Moreover, precision is evaluated using bootstrap analysis of in-vivo measurements. In the experiments, IVIM parameters are computed a) by using a segmented fit method and b) by performing a biexponential fit of the entire attenuation curve based on nonlinear least squares estimates. Irrespective of the fit method, the results demonstrate that reliability of IVIM parameter estimates is substantially improved by image denoising. The experiments show that the LPCA and the JREC algorithms perform in a similar manner and outperform the NLM-related methods. Relative to noisy data, accuracy of the IVIM parameters in the in-silico phantom improves after image denoising by 76–79%, 79–81%, 84–99% and precision by 74–80%, 80–83%, 84–95% for the perfusion fraction, the diffusion coefficient, and the pseudodiffusion coefficient, respectively, when the segmented fit method is used. Beyond that, the simulations reveal that denoising performance is not impeded by spatially inhomogeneous levels of Rician noise in the image. Since all investigated algorithms are freely available and work on magnitude data they can be readily applied in the clinical setting which may foster transition of IVIM imaging into clinical practice.

Reliable estimation of brain intravoxel incoherent motion parameters using denoised diffusion‐weighted MRI

“…As shown in Table , the estimated PF values obtained from the proposed method were consistent with values from the literature (ie, GM = 0.10–0.14 and WM = 0.07) . Similarly, the estimated D values obtained from the proposed method consistently agreed with values from the literature (ie, GM = 0.7–0.95 ×10 −3 and WM = 0.57–0.77 ×10 −3 mm 2 /s) .…”

“…As also shown in Figures S2 and S3, the simulation results of k‐space energy density and structural similarity index indicated that the kernel‐based postfiltering method applied to each parametric image did not lead to a severe loss of spatial information. Our experimental results also showed that all estimated IVIM parameters were within the range of physiologically acceptable values . No overestimation of the IVIM parameters was observed.…”

“…Our previous study showed that the proposed kernel‐based image denoising method could provide more reliable IVIM parameters than the method used by Reischauer and Gutzeit . However, we did not evaluate the performance of the proposed kernel‐based image denoising method using brain DW‐MRI data.…”

“…For example, constrained fitting methods, total variation‐based methods and Bayesian probability‐based methods were shown to provide improved parameter estimates. Several recent studies have also shown that denoising DW‐MRI data can improve the quality of parametric images . However, in addition to the problem of image noise, the reliable parameter estimation of low‐perfused tissues (eg, brain) remains a challenge .…”

“…Recently, one study showed that advanced image denoising algorithms including the local principal component analysis (LPCA) algorithm and the joint rank and edge constraints (JREC) algorithm can substantially improve the accuracy and precision of brain IVIM parameter estimates . However, only one volunteer was evaluated in that study, and furthermore the authors reported that the brain D* map suffered from a residual aliasing artifact . More evidence is required to ensure that image denoising can provide reliable brain IVIM parameter estimates.…”