Comparison of methods for estimation of the intravoxel incoherent motion (IVIM) diffusion coefficient (D) and perfusion fraction (f)

Objective

Intravoxel incoherent motion (IVIM) shows great potential in many applications, e.g., tumor tissue characterization. To reduce image-quality demands, various IVIM analysis approaches restricted to the diffusion coefficient (D) and the perfusion fraction (f) are increasingly being employed. In this work, the impact of estimation approach for D and f is studied.

Materials and methods

Four approaches for estimating D and f were studied: segmented IVIM fitting, least-squares fitting of a simplified IVIM model (sIVIM), and Bayesian fitting of the sIVIM model using marginal posterior modes or posterior means. The estimation approaches were evaluated in terms of bias and variability as well as ability for differentiation between tumor and healthy liver tissue using simulated and in vivo data.

Results

All estimation approaches had similar variability and ability for differentiation and negligible bias, except for the Bayesian posterior mean of f, which was substantially biased. Combined use of D and f improved tumor-to-liver tissue differentiation compared with using D or f separately.

Discussion

The similar performance between estimation approaches renders the segmented one preferable due to lower numerical complexity and shorter computational time. Superior tissue differentiation when combining D and f suggests complementary biologically relevant information.