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

Journal article
Authors Oscar Jalnefjord
Mats Andersson
Mikael Montelius
Göran Starck
Anna-Karin Elf
Viktor Johanson
Johanna Svensson
Maria Ljungberg
Published in Magnetic Resonance Materials in Physics, Biology and Medicine
Volume 31
Issue 6
Pages 715-723
ISSN 0968-5243
Publication year 2018
Published at Institute of Clinical Sciences, Department of Radiation Physics
Institute of Clinical Sciences, Department of Surgery
Institute of Clinical Sciences, Department of Oncology
Institute of Clinical Sciences, Department of Radiology
Pages 715-723
Language en
Links https://doi.org/10.1007/s10334-018-...
Keywords Diffusion magnetic resonance imaging, Monte Carlo method, Perfusion, Signal-to-noise ratio
Subject categories Radiological physics, Diagnostic radiology

Abstract

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.

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