SNR-weighted regularization of ADC estimates from double-echo in steady-state (DESS)

Abstract

Purpose: To improve the homogeneity and consistency of apparent diffusion coefficient (ADC) estimates in cartilage from the double-echo in steady-state (DESS) sequence by applying SNR-weighted regularization during post-processing. Methods: An estimation method that linearizes ADC estimates from DESS is used in conjunction with a smoothness constraint to suppress noise-induced variation in ADC estimates. Simulations, phantom scans, and in vivo scans are used to demonstrate how the method reduces ADC variability. Conventional diffusion-weighted echo-planar imaging (DW EPI) maps are acquired for comparison of mean and standard deviation (SD) of the ADC estimate. Results: Simulations and phantom scans demonstrated that the SNR-weighted regularization can produce homogenous ADC maps at varying levels of SNR, whereas non-regularized maps only estimate ADC accurately at high SNR levels. The in vivo maps showed that the SNR-weighted regularization produced ADC maps with similar heterogeneity to maps produced with standard DW EPI, but without the distortion of such reference scans. Conclusion: A linear approximation of a simplified model of the relationship between DESS signals allows for fast SNR-weighted regularization of ADC maps that reduces estimation error in relatively short T2 tissue such as cartilage.