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Dynamic PET simulator via tomographic emission projection for kinetic modeling and parametric image studies
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.ORCID iD: 0000-0001-9178-6683
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
2016 (English)In: Medical physics (Lancaster), ISSN 0094-2405, Vol. 43, no 6, 3104-3116 p.Article in journal (Refereed) Published
Abstract [en]

Purpose: To develop and evaluate a fast and simple tool called dPETSTEP (Dynamic PET Simulator ofTracers via Emission Projection), for dynamic PET simulations as an alternative to Monte Carlo (MC), useful for educational purposes and evaluation of the effects of the clinical environment,postprocessing choices, etc., on dynamic and parametric images.

Methods: The tool was developed in PETSTEP using both new and previously reported modules of PETSTEP (PET Simulator of Tracers via Emission Projection). Time activity curves are generated foreach voxel of the input parametric image, whereby effects of imaging system blurring, counting noise,scatters, randoms, and attenuation are simulated for each frame. Each frame is then reconstructed intoimages according to the user specified method, settings, and corrections. Reconstructed images werecompared to MC data, and simple Gaussian noised time activity curves (GAUSS).

Results: dPETSTEP was 8000 times faster than MC. Dynamic images from dPETSTEP had a root meansquare error that was within 4% on average of that of MC images, whereas the GAUSS images werewithin 11%. The average bias in dPETSTEP and MC images was the same, while GAUSS differed by 3% points. Noise profiles in dPETSTEP images conformed well to MC images, confirmed visually by scatterplot histograms, and statistically by tumor region of interest histogram comparisons that showed nosignificant differences (p < 0.01). Compared to GAUSS, dPETSTEP images and noise properties agreedbetter with MC.

Conclusions: The authors have developed a fast and easy one-stop solution for simulationsof dynamic PET and parametric images, and demonstrated that it generates both images andsubsequent parametric images with very similar noise properties to those of MC images, in afraction of the time. They believe dPETSTEP to be very useful for generating fast, simple, andrealistic results, however since it uses simple scatter and random models it may not be suitablefor studies investigating these phenomena. dPETSTEP can be downloaded free of cost from https://github.com/CRossSchmidtlein/dPETSTEP.

Place, publisher, year, edition, pages
American Association of Physicists in Medicine , 2016. Vol. 43, no 6, 3104-3116 p.
Keyword [en]
dynamic PET, simulation, PETSTEP, Monte Carlo, compartment modeling, parametric imaging
National Category
Medical Image Processing
Research subject
radiofysik
Identifiers
URN: urn:nbn:se:umu:diva-121069DOI: 10.1118/1.4950883PubMedID: 27277057OAI: oai:DiVA.org:umu-121069DiVA: diva2:930864
Funder
Swedish National Infrastructure for Computing (SNIC), 2015/1-328
Available from: 2016-05-25 Created: 2016-05-25 Last updated: 2016-08-26Bibliographically approved

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