Estimate of FDG Excretion by means of Compartmental Analysis and Ant Colony Optimization of Nuclear Medicine Data

Author:

Garbarino Sara12,Caviglia Giacomo1,Brignone Massimo3,Massollo Michela4,Sambuceti Gianmario5,Piana Michele12

Affiliation:

1. Dipartimento di Matematica, Università di Genova, Via Dodecaneso 35, 16146 Genova, Italy

2. CNR—SPIN, Via Dodecaneso 33, 16146 Genova, Italy

3. Dipartimento di Ingegneria Navale, Elettrica, Elettronica e delle Telecomunicazioni, Università di Genova, Via Opera Pia 11, 16145 Genova, Italy

4. IRCCS San Martino IST, Largo Rosanna Benzi 10, 16132 Genova, Italy

5. Dipartimento di Scienze della Salute, Università di Genova, Largo Rosanna Benzi 10, 16132 Genova, Italy

Abstract

[18F]fluoro-2-deoxy-D-glucose (FDG) is one of the most utilized tracers for positron emission tomography (PET) applications in oncology. FDG-PET relies on higher glycolytic activity in tumors compared to normal structures as the basis of image contrast. As a glucose analog, FDG is transported into malignant cells which typically exhibit an increased radioactivity. However, different from glucose, FDG is not reabsorbed by the renal system and is excreted to the bladder. The present paper describes a novel computational method for the quantitative assessment of this excretion process. The method is based on a compartmental analysis of FDG-PET data in which the excretion process is explicitly accounted for by the bladder compartment and on the application of an ant colony optimization (ACO) algorithm for the determination of the tracer coefficients describing the FDG transport effectiveness. The validation of this approach is performed by means of both synthetic data and real measurements acquired by a PET device for small animals (micro-PET). Possible oncological applications of the results are discussed in the final section.

Funder

Fondazione Cassa di Risparmio di Genova

Publisher

Hindawi Limited

Subject

Applied Mathematics,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,Modeling and Simulation,General Medicine

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