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

Author:

Conte MartinaORCID,Woodall Ryan T.ORCID,Gutova Margarita,Chen Bihong T.,Shiroishi Mark S.,Brown Christine E.,Munson Jennifer M.,Rockne Russell C.ORCID

Abstract

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.

Funder

National Institutes of Health

California Institute for Regenerative Medicine

Ministero dell’Istruzione, dell’Università e della Ricerca

Gruppo Nazionale per la Fisica Matematica

Publisher

Public Library of Science (PLoS)

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