UNLABELLED: A modified short dynamic protocol was defined and evaluated to predict kinetic parameters of (18)F-FDG metabolism from a dynamic data acquisition. METHODS: The evaluation included 151 datasets obtained from 60 patients examined with (18)F-FDG and a dynamic data acquisition protocol of 60 min. Standardized uptake values (SUVs) were calculated for the individual time frames, and a 2-compartment model was applied to the data. The kinetic parameters and the (18)F-FDG influx, calculated from the model data, served as the reference for the analysis. Correlation was analyzed for the SUVs and the reference data. Subset analysis identified time intervals that can be used to predict the reference parameters based on a second-order polynomial function. RESULTS: Significant correlations were noted for SUVs and (18)F-FDG influx, vascular fraction (VB), and the rate constant k1. The influx was associated mainly with SUVs of late acquisition times, whereas higher correlations were noted for early acquisition intervals and VB, as well as k1. A short dynamic acquisition protocol was defined on the basis of a short dynamic sequence 1-10 min after tracer injection and a static acquisition 56-60 min after tracer application. The correlation coefficients exceeded 0.9 for influx, VB, and k1 when the SUVs of the input area (blood) and the target area were used to predict the kinetic parameters. CONCLUSION: A short dynamic data acquisition protocol can be used to obtain more detailed information about (18)F-FDG kinetics. The results demonstrate that (18)F-FDG influx, VB, and k1 can be estimated with high accuracy from SUVs.
UNLABELLED: A modified short dynamic protocol was defined and evaluated to predict kinetic parameters of (18)F-FDG metabolism from a dynamic data acquisition. METHODS: The evaluation included 151 datasets obtained from 60 patients examined with (18)F-FDG and a dynamic data acquisition protocol of 60 min. Standardized uptake values (SUVs) were calculated for the individual time frames, and a 2-compartment model was applied to the data. The kinetic parameters and the (18)F-FDG influx, calculated from the model data, served as the reference for the analysis. Correlation was analyzed for the SUVs and the reference data. Subset analysis identified time intervals that can be used to predict the reference parameters based on a second-order polynomial function. RESULTS: Significant correlations were noted for SUVs and (18)F-FDG influx, vascular fraction (VB), and the rate constant k1. The influx was associated mainly with SUVs of late acquisition times, whereas higher correlations were noted for early acquisition intervals and VB, as well as k1. A short dynamic acquisition protocol was defined on the basis of a short dynamic sequence 1-10 min after tracer injection and a static acquisition 56-60 min after tracer application. The correlation coefficients exceeded 0.9 for influx, VB, and k1 when the SUVs of the input area (blood) and the target area were used to predict the kinetic parameters. CONCLUSION: A short dynamic data acquisition protocol can be used to obtain more detailed information about (18)F-FDG kinetics. The results demonstrate that (18)F-FDG influx, VB, and k1 can be estimated with high accuracy from SUVs.
Authors: T Thireou; G Kontaxakis; L G Strauss; A Dimitrakopoulou-Strauss; S Pavlopoulos; A Santos Journal: Med Biol Eng Comput Date: 2005-01 Impact factor: 2.602
Authors: S Bisdas; T Nägele; H-P Schlemmer; A Boss; C D Claussen; B Pichler; U Ernemann Journal: AJNR Am J Neuroradiol Date: 2009-11-26 Impact factor: 3.825