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A Monte Carlo study of the dependence of early frame sampling on uncertainty and bias in pharmacokinetic parameters from dynamic PET
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.ORCID iD: 0000-0002-3731-3612
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York 10065, USA.
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
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2015 (English)In: Journal of Nuclear Medicine Technology, ISSN 0091-4916, E-ISSN 1535-5675, Vol. 43, no 1, 53-60 p.Article in journal (Refereed) Published
Abstract [en]

Compartmental modeling of dynamic PET data enables quantifi- cation of tracer kinetics in vivo, through the calculated model parameters. In this study, we aimed to investigate the effect of early frame sampling and reconstruction method on pharmacokinetic parameters obtained from a 2-tissue model, in terms of bias and uncertainty (SD). Methods: The GATE Monte Carlo software was used to simulate 2 · 15 dynamic 3′-deoxy-3′-18F-fluorothymidine (18F-FLT) brain PET studies, typical in terms of noise level and kinetic parameters. The data were reconstructed by both 3- dimensional (3D) filtered backprojection with reprojection (3DRP) and 3D ordered-subset expectation maximization (OSEM) into 6 dynamic image sets with different early frame durations of 1, 2, 4, 6, 10, and 15 s. Bias and SD were evaluated for fitted parameter estimates, calculated from regions of interest. Results: The 2-tissue-model parameter estimates K1, k2, and fraction of arterial blood in tissue depended on early frame sampling, and a sampling of 6–15 s generally minimized bias and SD. The shortest sampling of 1 s yielded a 25% and 42% larger bias than the other schemes, for 3DRP and OSEM, respectively, and a parameter uncertainty that was 10%–70% higher. The schemes from 4 to 15 s were generally not significantly different in regards to bias and SD. Typically, the reconstruction method 3DRP yielded less framesampling dependence and less uncertain results, compared with OSEM, but was on average more biased. Conclusion: Of the 6 sampling schemes investigated in this study, an early frame duration of 6–15 s generally kept both bias and uncertainty to a minimum, for both 3DRP and OSEM reconstructions. Veryshort frames of 1 s should be avoided because they typically resulted in the largest parameter bias and uncertainty. Furthermore, 3DRP may be p

Place, publisher, year, edition, pages
2015. Vol. 43, no 1, 53-60 p.
Keyword [en]
dynamic PET, Monte Carlo; GATE, compartment modeling, frame sampling
National Category
Other Physics Topics Medical Image Processing
Research subject
URN: urn:nbn:se:umu:diva-95128DOI: 10.2967/jnmt.114.141754OAI: diva2:757504
Swedish National Infrastructure for Computing (SNIC), HPC2N-2009-001
Available from: 2014-10-22 Created: 2014-10-22 Last updated: 2016-05-26Bibliographically approved
In thesis
1. Quantitative methods for tumor imaging with dynamic PET
Open this publication in new window or tab >>Quantitative methods for tumor imaging with dynamic PET
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Kvantitativa metoder för tumöravbildning med dynamisk PET
Abstract [en]

There is always a need and drive to improve modern cancer care. Dynamic positron emission tomography (PET) offers the advantage of in vivo functional imaging, combined with the ability to follow the physiological processes over time. In addition, by applying tracer kinetic modeling to the dynamic PET data, thus estimating pharmacokinetic parameters associated to e.g. glucose metabolism, cell proliferation etc., more information about the tissue's underlying biology and physiology can be determined. This supplementary information can potentially be a considerable aid when it comes to the segmentation, diagnosis, staging, treatment planning, early treatment response monitoring and follow-up of cancerous tumors.

We have found it feasible to use kinetic parameters for semi-automatic tumor segmentation, and found parametric images to have higher contrast compared to static PET uptake images. There are however many possible sources of errors and uncertainties in kinetic parameters obtained through compartment modeling of dynamic PET data. The variation in the number of detected photons caused by the random nature of radioactive decay, is of course always a major source. Other sources may include: the choice of an appropriate model that is suitable for the radiotracer in question, camera detectors and electronics, image acquisition protocol, image reconstruction algorithm with corrections (attenuation, random and scattered coincidences, detector uniformity, decay) and so on. We have found the early frame sampling scheme in dynamic PET to affect the bias and uncertainty in calculated kinetic parameters, and that scatter corrections are necessary for most but not all parameter estimates. Furthermore, analytical image reconstruction algorithms seem more suited for compartment modeling applications compared to iterative algorithms.

This thesis and included papers show potential applications and tools for quantitative pharmacokinetic parameters in oncology, and help understand errors and uncertainties associated with them. The aim is to contribute to the long-term goal of enabling the use of dynamic PET and pharmacokinetic parameters for improvements of today's cancer care.

Abstract [sv]

Det finns alltid ett behov och en strävan att förbättra dagens cancervård. Dynamisk positronemissionstomografi (PET) medför fördelen av in vivo funktionell avbilning, kombinerad med möjligheten att följa fysiologiska processer över tiden. Genom att därtill tillämpa kinetisk modellering på det dynamiska PET-datat, och därigenom skatta farmakokinetiska parametrar associerade till glukosmetabolism, cellproliferation etc., kan ytterligare information om vävnadens underliggande biologi och fysiologi bestämmas. Denna kompletterande information kan potentiellt vara till stor nytta för segmentering, diagnos, stadieindelning, behandlingsplanering, monitorering av tidig behandlingsrespons samt uppföljning av cancertumörer.

Vi fann det möjligt att använda kinetiska parametrar för semi-automatisk tumörsegmentering, och fann även att parametriska bilder hade högre kontrast jämfört med upptagsbilder från statisk PET. Det finns dock många möjliga källor till osäkerheter och fel i kinetiska parametrar som beräknats genom compartment-modellering av dynamisk PET. En av de största källorna är det radioaktiva sönderfallets slumpmässiga natur som orsakar variationer i antalet detekterade fotoner. Andra källor inkluderar valet av compartment-modell som är lämplig för den aktuella radiotracern, PET-kamerans detektorer och elektronik, bildtagningsprotokoll, bildrekonstruktionsalgoritm med tillhörande korrektioner (attenuering, slumpmässig och spridd strålning, detektorernas likformighet, sönderfall) och så vidare. Vi fann att tidssamplingsschemat för tidiga bilder i dynamisk PET påverkar både fel och osäkerhet i beräknade kinetiska parametrar, och att bildkorrektioner för spridd strålning är nödvändigt för de flesta men inte alla parametrar. Utöver detta verkar analytiska bildrekonstruktionsalgoritmer vara bättre lämpade för tillämpningar som innefattar compartment-modellering i jämförelse med iterativa algoritmer.

Denna avhandling med inkluderade artiklar visar möjliga tillämpningar och verktyg för kvantitativa kinetiska parametrar inom onkologiområdet. Den bidrar också till förståelsen av fel och osäkerheter associerade till dem. Syftet är att bidra till det långsiktiga målet att möjliggöra användandet av dynamisk PET och farmakokinetiska parametrar för att förbättra dagens cancervård.

Place, publisher, year, edition, pages
Umeå: Umeå Universitet, 2014. 94 p.
Umeå University medical dissertations, ISSN 0346-6612 ; 1683
Dynamic positron emission tomography, PET, tumor imaging, compartment modeling, Monte Carlo
National Category
Medical Image Processing Other Physics Topics
Research subject
urn:nbn:se:umu:diva-95126 (URN)978-91-7601-160-7 (ISBN)
Public defence
2014-12-12, Hörsal Betula, Norrlands Universitetssjukhus, Umeå, 09:00 (Swedish)
Swedish National Infrastructure for Computing (SNIC), HPC2N-2009-001Swedish National Infrastructure for Computing (SNIC), 2013/1-234Swedish National Infrastructure for Computing (SNIC), 2014/1-260
Available from: 2014-11-21 Created: 2014-10-22 Last updated: 2016-05-26Bibliographically approved

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Häggström, IdaAxelsson, JanKarlsson, MikaelGarpebring, AndersJohansson, LennartLarsson, Anne
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