Compartmental analysis of dynamic nuclear medicine data: models and identifiability
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 non-linear 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.
Keywords
Cite
@article{arxiv.1603.02266,
title = {Compartmental analysis of dynamic nuclear medicine data: models and identifiability},
author = {Fabrice Delbary and Sara Garbarino and Valentina Vivaldi},
journal= {arXiv preprint arXiv:1603.02266},
year = {2018}
}