Compartmental analysis of renal physiology using nuclear medicine data and statistical optimization
Sara Garbarino, Giacomo Caviglia, Massimo Brignone, Michela Massollo, Gianmario Sambuceti, Michele Piana
arXiv:1212.39671 citationsh-index: 55
Originality Synthesis-oriented
AI Analysis
For researchers in nuclear medicine and renal physiology, this provides a validated modeling framework, but it is an incremental methodological contribution.
The paper presents a compartmental modeling approach for nuclear medicine data using spectral analysis and statistical optimization, validated on renal physiology with synthetic data and micro-PET experiments in mice.
This paper describes a general approach to the compartmental modeling of nuclear data based on spectral analysis and statistical optimization. We utilize the renal physiology as test case and validate the method against both synthetic data and real measurements acquired during two micro-PET experiments with murine models.