Image denoising and model-independent parameterization for IVIM MRI

Abstract

Objective: To improve intravoxel incoherent motion imaging (IVIM) magnetic resonance Imaging (MRI) quality using a new image denoising technique and model-independent parameterization of the signal versus b-value curve. Approach: IVIM images were acquired for 13 head-and-neck patients prior to radiotherapy. Post-radiotherapy scans were also acquired for five patients. Images were denoised prior to parameter fitting using neural blind deconvolution, a method of solving the mathematical problem of blind deconvolution using neural networks. The signal decay curve was then quantified in terms of several area under the curve (AUC) parameters. Improvements in image quality were assessed using blind image quality metrics, total variation (TV), and the strength of correlations between IVIM parameters in parotid glands with radiotherapy dose levels. The accuracy of blur kernel predictions was assessed by the ability to recover artificial "pseudokernels" which had been applied to denoised images. AUC parameters were compared with apparent diffusion coefficient (ADC), biexponential, and triexponential model parameters in terms of their correlations with dose, contrast-to-noise (CNR) ratios inside and outside parotid glands, and their relative importance as determined via principal component analysis.
Main Results: Image denoising improved blind image quality metrics, smoothed the signal versus b-value curve, and strengthened correlations between IVIM parameters and dose. Image TV was reduced and parameter CNRs generally increased following denoising. AUC parameter changes had higher correlations with dose and higher relative importance than traditional IVIM parameters. Significance: IVIM parameters have high variability in the literature and perfusion-related parameters are difficult to interpret. Describing the signal versus b-value curve with model-independent parameters like the AUC and preprocessing images with denoising techniques could potentially benefit IVIM image parameterization in terms of reproducibility and functional utility.