6533b7d7fe1ef96bd126912f
RESEARCH PRODUCT
Compartmental analysis of dynamic nuclear medicine data: Models and identifiability
Sara GarbarinoFabrice DelbaryValentina Vivaldisubject
Regularization (mathematics)Quantitative Biology - Quantitative Methods030218 nuclear medicine & medical imagingTheoretical Computer ScienceData modeling03 medical and health sciences0302 clinical medicinecompartmental analysis; identifiability; nuclear medicine dataTRACERFOS: Mathematicscompartmental analysisUniquenessMathematics - Numerical AnalysisMathematical PhysicsQuantitative Methods (q-bio.QM)Mathematicsbusiness.industryApplied MathematicsBiological tissueNumerical Analysis (math.NA)Inverse problemidentifiabilityComputer Science ApplicationsNonlinear systemnuclear medicine dataFOS: Biological sciencesSignal ProcessingIdentifiabilityNuclear medicinebusiness030217 neurology & neurosurgerydescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2016-01-01 |