Determination of the optimal set of b-values for ADC mapping under a Rician noise assumption

Abstract

Mapping of the apparent diffusion coefficient (ADC), estimated from a set of diffusion-weighted (DW) images acquired with different b-values, often suffers from low SNR, which can introduce large variance in ADC maps. Unfortunately, there is no consensus on the optimal b-values to maximize the noise performance of ADC map. In this work, we determine the optimal b-values to maximize the noise performance of ADC mapping by using a Cramér-Rao Lower Bound (CRLB) approach under realistic noise assumptions. The strong agreement between the CRLB-based analysis, Monte-Carlo simulations, and ADC phantom experiment, suggests the utility of this approach to optimize DW-MRI acquisitions.