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Compartmental analysis of dynamic nuclear medicine data: models and identifiability

Delbary, F; Garbarino, S; Vivaldi, V; (2016) Compartmental analysis of dynamic nuclear medicine data: models and identifiability. Inverse Problems , 32 (12) 10.1088/0266-5611/32/12/125010. Green open access

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Abstract

Compartmental models based on tracer mass balance are extensively used in clinical and pre-clinical nuclear medicine in order to obtain quantitative information on tracer metabolism in the biological tissue. This paper is the first of a series of two that deal with the problem of tracer coefficient estimation via compartmental modelling in an inverse problem framework. Specifically, here we discuss the identifiability problem for a general n-dimension compartmental system and provide uniqueness results in the case of two-compartment and three-compartment compartmental models. The second paper will utilize this framework in order to show how nonlinear regularization schemes can be applied to obtain numerical estimates of the tracer coefficients in the case of nuclear medicine data corresponding to brain, liver and kidney physiology.

Type: Article
Title: Compartmental analysis of dynamic nuclear medicine data: models and identifiability
Open access status: An open access version is available from UCL Discovery
DOI: 10.1088/0266-5611/32/12/125010
Publisher version: http://doi.org/10.1088/0266-5611/32/12/125010
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: compartmental analysis, nuclear medicine data, identifiability
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: http://discovery.ucl.ac.uk/id/eprint/10045317
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