Compartmental analysis of dynamic nuclear medicine data: regularization procedure and application to physiology

TL;DR

This paper presents a regularization method for estimating tracer coefficients in compartmental models used in nuclear medicine, with applications to cerebral, hepatic, and renal functions based on FDG-PET data.

Contribution

It introduces a regularized Multivariate Gauss Newton scheme for numerical tracer coefficient estimation in compartmental models, extending previous work on identifiability.

Findings

Effective estimation of tracer coefficients in various organ systems.

Application to FDG-PET data in murine models.

Improved numerical stability in compartmental analysis.

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 second of a series of two that deal with the problem of tracer coefficient estimation via compartmental modelling in an inverse problem framework. While the previous work was devoted to the discussion of identifiability issues for 2, 3 and n-dimension compartmental systems, here we discuss the problem of numerically determining the tracer coefficients by means of a general regularized Multivariate Gauss Newton scheme. In this paper, applications concerning cerebral, hepatic and renal functions are considered, involving experimental measurements on FDG-PET data on different set of murine models.