Astrocytic tracer dynamics estimated from [1-11C]-acetate PET measurements
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Astrocytic tracer dynamics estimated from [1-11C]-acetate PET measurements. / Arnold, Andrea; Calvetti, Daniela; Gjedde, Albert; Iversen, Peter; Somersalo, Erkki.
In: Mathematical Medicine and Biology (Print), Vol. 32, No. 4, 12.2015, p. 367-382.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Astrocytic tracer dynamics estimated from [1-11C]-acetate PET measurements
AU - Arnold, Andrea
AU - Calvetti, Daniela
AU - Gjedde, Albert
AU - Iversen, Peter
AU - Somersalo, Erkki
PY - 2015/12
Y1 - 2015/12
N2 - We address the problem of estimating the unknown parameters of a model of tracer kinetics from sequences of positron emission tomography (PET) scan data using a statistical sequential algorithm for the inference of magnitudes of dynamic parameters. The method, based on Bayesian statistical inference, is a modification of a recently proposed particle filtering and sequential Monte Carlo algorithm, where instead of preassigning the accuracy in the propagation of each particle, we fix the time step and account for the numerical errors in the innovation term. We apply the algorithm to PET images of [1-11C]-acetate-derived tracer accumulation, estimating the transport rates in a three-compartment model of astrocytic uptake and metabolism of the tracer for a cohort of 18 volunteers from 3 groups, corresponding to healthy control individuals, cirrhotic liver and hepatic encephalopathy patients. The distribution of the parameters for the individuals and for the groups presented within the Bayesian framework support the hypothesis that the parameters for the hepatic encephalopathy group follow a significantly different distribution than the other two groups. The biological implications of the findings are also discussed.
AB - We address the problem of estimating the unknown parameters of a model of tracer kinetics from sequences of positron emission tomography (PET) scan data using a statistical sequential algorithm for the inference of magnitudes of dynamic parameters. The method, based on Bayesian statistical inference, is a modification of a recently proposed particle filtering and sequential Monte Carlo algorithm, where instead of preassigning the accuracy in the propagation of each particle, we fix the time step and account for the numerical errors in the innovation term. We apply the algorithm to PET images of [1-11C]-acetate-derived tracer accumulation, estimating the transport rates in a three-compartment model of astrocytic uptake and metabolism of the tracer for a cohort of 18 volunteers from 3 groups, corresponding to healthy control individuals, cirrhotic liver and hepatic encephalopathy patients. The distribution of the parameters for the individuals and for the groups presented within the Bayesian framework support the hypothesis that the parameters for the hepatic encephalopathy group follow a significantly different distribution than the other two groups. The biological implications of the findings are also discussed.
KW - parameter estimation
KW - tracer kinetics
KW - PET imaging
KW - particle filters
KW - sequential Monte Carlo (SMC)
U2 - 10.1093/imammb/dqu021
DO - 10.1093/imammb/dqu021
M3 - Journal article
C2 - 25424579
VL - 32
SP - 367
EP - 382
JO - Mathematical Medicine and Biology
JF - Mathematical Medicine and Biology
SN - 1477-8599
IS - 4
ER -
ID: 160926964