The influence of time sampling scheme on kinetic parameters obtained from compartmental modeling of a dynamic PET study: a Monte Carlo study
2012 (English)In: IEEE Nuclear Science Symposium Conference Record / [ed] B. Yu, Anaheim: IEEE conference proceedings, 2012, 3101-3107 p.Conference paper (Refereed)
Compartmental modeling of dynamic PET data enables quantification of tracer kinetics in vivo, through the obtained model parameters. The dynamic data is sorted into frames during or after the acquisition, with a sampling interval usually ranging from 10 s to 300 s. In this study we wanted to investigate the effect of the chosen sampling interval on kinetic parameters obtained from a 2-tissue model, in terms of bias and standard deviation, using a complete Monte Carlo simulated dynamic F-18-FLT PET study. The results show that the bias and standard deviation in parameter K-1 is small regardless of sampling scheme or noise in the time-activity curves (TACs), and that the bias and standard deviation in k(4) is large for all cases. The bias in V-a is clearly dependent on sampling scheme, increasing for increased sampling interval. In general, a too short sampling interval results in very noisy images and a large bias of the parameter estimate, and a too long sampling interval also increases bias. Noise in the TACs is the largest source of bias.
Place, publisher, year, edition, pages
Anaheim: IEEE conference proceedings, 2012. 3101-3107 p.
, IEEE Nuclear Science Symposium Conference Record, ISSN 1082-3654
PET, dynamic PET, Monte Carlo, GATE, compartment model, time sampling, sampling interval, AIF, FLT
Medical Image Processing
Research subject radiofysik
IdentifiersURN: urn:nbn:se:umu:diva-84150DOI: 10.1109/NSSMIC.2012.6551707ISI: 000326814203044ISBN: 978-1-4673-2030-6OAI: oai:DiVA.org:umu-84150DiVA: diva2:680076
IEEE Nuclear Science Symposium / Medical Imaging Conference Record (NSS/MIC) / 19th Room-Temperature Semiconductor X-ray and Gamma-ray Detector Workshop, OCT 29-NOV 03, 2012, Anaheim, CA
FunderSwedish National Infrastructure for Computing (SNIC), HPC2N-2009-001