Maximum Likelihood Analysis Provides Accurate ADC Estimates from Diffusion-Weighted Prostate Images Acquired with Multichannel Coils

Louisa Bokacheva¹, Yousef Mazaheri^1,2, Hedvig Hricak², Jason Koutcher¹

Diffusion-weighted (DW) MR images are contaminated with Rician noise, which leads to bias in ADC estimates. We explore accuracy and precision of calculating ADC from DW images acquired with multiple receiver channels using noise-corrected maximum likelihood estimation and uncorrected nonlinear least-squares fitting and log-linear fitting. Using Monte Carlo simulations, phantom and in vivo imaging of human prostate we demonstrate that accounting for Rician noise is important for images with variable SNR, for data acquired with phased arrays, and for achieving the maximum contrast between tissues with low and high ADC, which is often required for discriminating cancer and benign tissues on ADC maps.