Structural and practical identifiability of contrast transport models for DCE-MRI.

Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet ISSN: 1553-7358 (Electronic) Linking ISSN: 1553734X NLM ISO Abbreviation: PLoS Comput Biol Subsets: MEDLINE

Original Publication: San Francisco, CA : Public Library of Science, [2005]-

Contrast Media*/chemistry ; Contrast Media*/pharmacokinetics ; Magnetic Resonance Imaging*/methods; Humans ; Models, Biological ; Computational Biology ; Computer Simulation

Competing Interests: The authors have declared that no competing interests exist.
Contrast transport models are widely used to quantify blood flow and transport in dynamic contrast-enhanced magnetic resonance imaging. These models analyze the time course of the contrast agent concentration, providing diagnostic and prognostic value for many biological systems. Thus, ensuring accuracy and repeatability of the model parameter estimation is a fundamental concern. In this work, we analyze the structural and practical identifiability of a class of nested compartment models pervasively used in analysis of MRI data. We combine artificial and real data to study the role of noise in model parameter estimation. We observe that although all the models are structurally identifiable, practical identifiability strongly depends on the data characteristics. We analyze the impact of increasing data noise on parameter identifiability and show how the latter can be recovered with increased data quality. To complete the analysis, we show that the results do not depend on specific tissue characteristics or the type of enhancement patterns of contrast agent signal.
(Copyright: © 2024 Conte et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Update of: bioRxiv. 2023 Dec 19:2023.12.19.572294. doi: 10.1101/2023.12.19.572294. (PMID: 38187554)

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P30 CA033572 United States CA NCI NIH HHS; R01 CA254271 United States CA NCI NIH HHS; R01 NS115971 United States NS NINDS NIH HHS

0 (Contrast Media)

Date Created: 20240515 Date Completed: 20240528 Latest Revision: 20240530

20250114

PMC11132485

10.1371/journal.pcbi.1012106

38748755