umu.sePublications
Change search
Link to record
Permanent link

Direct link
BETA
Publications (10 of 12) Show all publications
Leffler, K., Häggström, I. & Yu, J. (2018). Intelligent data sampling promotes accelerated medical imaging: sharper positron emission tomography. In: : . Paper presented at The 6th Swedish Workshop on Data Science (SweDS18), November 20-21, 2018, Umeå Sweden.
Open this publication in new window or tab >>Intelligent data sampling promotes accelerated medical imaging: sharper positron emission tomography
2018 (English)Conference paper, Oral presentation with published abstract (Refereed)
National Category
Probability Theory and Statistics Medical Image Processing Signal Processing
Research subject
Mathematical Statistics
Identifiers
urn:nbn:se:umu:diva-154631 (URN)
Conference
The 6th Swedish Workshop on Data Science (SweDS18), November 20-21, 2018, Umeå Sweden
Projects
Statistical modelling and intelligent data sampling in MRI and PET measurements for cancer therapy assessment
Funder
Swedish Research Council, 340-2013-534
Available from: 2018-12-20 Created: 2018-12-20 Last updated: 2018-12-20
Schmidtlein, C. R., Turner, J. N., Thompson, M. O., Mandal, K. C., Häggström, I., Zhang, J., . . . Krol, A. (2017). Initial performance studies of a wearable brain positron emission tomography camera based on autonomous thin-film digital Geiger avalanche photodiode arrays. Journal of Medical Imaging, 4(1), Article ID 011003.
Open this publication in new window or tab >>Initial performance studies of a wearable brain positron emission tomography camera based on autonomous thin-film digital Geiger avalanche photodiode arrays
Show others...
2017 (English)In: Journal of Medical Imaging, ISSN 2329-4302, E-ISSN 2329-4310, Vol. 4, no 1, article id 011003Article in journal (Refereed) Published
Abstract [en]

Using analytical and Monte Carlo modeling, we explored performance of a lightweight wearable helmet-shaped brain positron emission tomography (PET), or BET camera, based on thin-film digital Geiger avalanche photodiode arrays with Lutetium-yttrium oxyorthosilicate (LYSO) or LaBr3 scintillators for imaging in vivo human brain function of freely moving and acting subjects. We investigated a spherical cap BET and cylindrical brain PET (CYL) geometries with 250-mm diameter. We also considered a clinical whole-body (WB) LYSO PET/CT scanner. The simulated energy resolutions were 10.8% (LYSO) and 3.3% (LaBr3), and the coincidence window was set at 2 ns. The brain was simulated as a water sphere of uniform F-18 activity with a radius of 100 mm. We found that BET achieved >40% better noise equivalent count (NEC) performance relative to the CYL and >800% than WB. For 10-mm-thick LaBr3 equivalent mass systems, LYSO (7-mm thick) had similar to 40% higher NEC than LaBr3. We found that 1 x 1 x 3 mm scintillator crystals achieved similar to 1.1 mm full-width-half-maximum spatial resolution without parallax errors. Additionally, our simulations showed that LYSO generally outperformed LaBr3 for NEC unless the timing resolution for LaBr3 was considerably smaller than that presently used for LYSO, i.e., well below 300 ps.

Place, publisher, year, edition, pages
SPIE - International Society for Optical Engineering, 2017
Keywords
Positron emission tomography, Sensors, Scintillators, Brain, Photons, Imaging systems, Neuroimaging
National Category
Medical Equipment Engineering
Identifiers
urn:nbn:se:umu:diva-133937 (URN)10.1117/1.JMI.4.1.011003 (DOI)000392230300002 ()27921074 (PubMedID)
Available from: 2017-04-21 Created: 2017-04-21 Last updated: 2018-06-09Bibliographically approved
Häggström, I., Beattie, B. J. & Schmidtlein, C. R. (2016). Dynamic PET simulator via tomographic emission projection for kinetic modeling and parametric image studies. Medical physics (Lancaster), 43(6), 3104-3116
Open this publication in new window or tab >>Dynamic PET simulator via tomographic emission projection for kinetic modeling and parametric image studies
2016 (English)In: Medical physics (Lancaster), ISSN 0094-2405, Vol. 43, no 6, p. 3104-3116Article 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
Keywords
dynamic PET, simulation, PETSTEP, Monte Carlo, compartment modeling, parametric imaging
National Category
Medical Image Processing
Research subject
radiofysik
Identifiers
urn:nbn:se:umu:diva-121069 (URN)10.1118/1.4950883 (DOI)000401300500042 ()27277057 (PubMedID)
Funder
Swedish National Infrastructure for Computing (SNIC), 2015/1-328
Available from: 2016-05-25 Created: 2016-05-25 Last updated: 2018-06-07Bibliographically approved
Häggström, I., Axelsson, J., Schmidtlein, R., Karlsson, M., Garpebring, A., Johansson, L., . . . Larsson, A. (2015). A Monte Carlo study of the dependence of early frame sampling on uncertainty and bias in pharmacokinetic parameters from dynamic PET. Journal of Nuclear Medicine Technology, 43(1), 53-60
Open this publication in new window or tab >>A Monte Carlo study of the dependence of early frame sampling on uncertainty and bias in pharmacokinetic parameters from dynamic PET
Show others...
2015 (English)In: Journal of Nuclear Medicine Technology, ISSN 0091-4916, E-ISSN 1535-5675, Vol. 43, no 1, p. 53-60Article 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

Keywords
dynamic PET, Monte Carlo; GATE, compartment modeling, frame sampling
National Category
Other Physics Topics Medical Image Processing
Research subject
radiofysik
Identifiers
urn:nbn:se:umu:diva-95128 (URN)10.2967/jnmt.114.141754 (DOI)
Funder
Swedish National Infrastructure for Computing (SNIC), HPC2N-2009-001
Available from: 2014-10-22 Created: 2014-10-22 Last updated: 2018-06-07Bibliographically approved
Berthon, B., Häggström, I., Apte, A., Beattie, B. J., Kirov, A. S., Humm, J. L., . . . Schmidtlein, C. R. (2015). PETSTEP: generation of synthetic PET lesions for fast evaluation of segmentation methods. Physica medica (Testo stampato), 31(8), 969-980
Open this publication in new window or tab >>PETSTEP: generation of synthetic PET lesions for fast evaluation of segmentation methods
Show others...
2015 (English)In: Physica medica (Testo stampato), ISSN 1120-1797, E-ISSN 1724-191X, Vol. 31, no 8, p. 969-980Article in journal (Refereed) Published
Abstract [en]

Purpose: This work describes PETSTEP (PET Simulator of Tracers via Emission Projection): a faster and more accessible alternative to Monte Carlo (MC) simulation generating realistic PET images, for studies assessing image features and segmentation techniques.

Methods: PETSTEP was implemented within Matlab as open source software. It allows generating threedimensional PET images from PET/CT data or synthetic CT and PET maps, with user-drawn lesions and user-set acquisition and reconstruction parameters. PETSTEP was used to reproduce images of the NEMA body phantom acquired on a GE Discovery 690 PET/CT scanner, and simulated with MC for the GE Discovery LS scanner, and to generate realistic Head and Neck scans. Finally the sensitivity (S) and Positive Predictive Value (PPV) of three automatic segmentation methods were compared when applied to the scanner-acquired and PETSTEP-simulated NEMA images.

Results: PETSTEP produced 3D phantom and clinical images within 4 and 6 min respectively on a single core 2.7 GHz computer. PETSTEP images of the NEMA phantom had mean intensities within 2% of the scanner-acquired image for both background and largest insert, and 16% larger background Full Width at Half Maximum. Similar results were obtained when comparing PETSTEP images to MC simulated data. The S and PPV obtained with simulated phantom images were statistically significantly lower than for the original images, but led to the same conclusions with respect to the evaluated segmentation methods.

Conclusions: PETSTEP allows fast simulation of synthetic images reproducing scanner-acquired PET data and shows great promise for the evaluation of PET segmentation methods.

Keywords
Positron emission tomography, Digital phantoms, Simulation, Image segmentation, Synthetic lesions
National Category
Other Physics Topics Cancer and Oncology Radiology, Nuclear Medicine and Medical Imaging
Research subject
radiofysik
Identifiers
urn:nbn:se:umu:diva-95493 (URN)10.1016/j.ejmp.2015.07.139 (DOI)000366660400017 ()26321409 (PubMedID)
Funder
Swedish National Infrastructure for Computing (SNIC), 2014/1-260
Available from: 2014-10-30 Created: 2014-10-30 Last updated: 2018-06-07Bibliographically approved
Berthon, B., Häggström, I., Apte, A., Beattie, B., Kirov, A., Humm, J., . . . Schmidtlein, C. (2014). A Fast Positron Emission Tomography Simulator for Synthetic Lesion Simulation. European Journal of Nuclear Medicine and Molecular Imaging, 41(2), S367-S367
Open this publication in new window or tab >>A Fast Positron Emission Tomography Simulator for Synthetic Lesion Simulation
Show others...
2014 (English)In: European Journal of Nuclear Medicine and Molecular Imaging, ISSN 1619-7070, E-ISSN 1619-7089, Vol. 41, no 2, p. S367-S367Article in journal, Meeting abstract (Other academic) Published
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:umu:diva-100989 (URN)000348841901029 ()
External cooperation:
Funder
Swedish National Infrastructure for Computing (SNIC)
Available from: 2015-03-16 Created: 2015-03-16 Last updated: 2018-06-07Bibliographically approved
Häggström, I., Schmidtlein, C. R., Karlsson, M. & Larsson, A. (2014). Compartment Modeling of Dynamic Brain PET: The Effect of Scatter Corrections on Parameter Errors. In: : . Paper presented at AAPM. AAPM
Open this publication in new window or tab >>Compartment Modeling of Dynamic Brain PET: The Effect of Scatter Corrections on Parameter Errors
2014 (English)Conference paper, Poster (with or without abstract) (Other academic)
Abstract [en]

Purpose: To investigate the effects of corrections for random and scattered coincidences on kinetic parameters in brain tumors, by using ten Monte Carlo (MC) simulated dynamic FLT-PET brain scans.

 

Methods: The GATE MC software was used to simulate ten repetitions of a 1 hour dynamic FLT-PET scan of a voxelized head phantom. The phantom comprised six normal head tissues, plus inserted regions for blood and tumor tissue. Different time-activity-curves (TACs) for all eight tissue types were used in the simulation and were generated in Matlab using a 2-tissue model with preset parameter values (K1,k2,k3,k4,Va,Ki). The PET data was reconstructed into 28 frames by both ordered-subset expectation maximization (OSEM) and 3D filtered back-projection (3DFBP). Five image sets were reconstructed, all with normalization and different additional corrections C (A=attenuation, R=random, S=scatter): Trues (AC), trues+randoms (ARC), trues+scatters (ASC), total counts (ARSC) and total counts (AC). Corrections for randoms and scatters were based on real random and scatter sinograms that were back-projected, blurred and then forward projected and scaled to match the real counts. Weighted non-linear-least-squares fitting of TACs from the blood and tumor regions was used to obtain parameter estimates.

 

Results: The bias was not significantly different for trues (AC), trues+randoms (ARC), trues+scatters (ASC) and total counts (ARSC) for either 3DFBP or OSEM (p<0.05). Total counts with only AC stood out however, with an up to 160% larger bias. In general, there was no difference in bias found between 3DFBP and OSEM, except in parameter Va and Ki.

 

Conclusion: According to our results, the methodology of correcting the PET data for randoms and scatters performed well for the dynamic images where frames have much lower counts compared to static images. Generally, no bias was introduced by the corrections and their importance was emphasized since omitting them increased bias extensively.

Place, publisher, year, edition, pages
AAPM, 2014
National Category
Medical Image Processing
Research subject
radiofysik
Identifiers
urn:nbn:se:umu:diva-98532 (URN)
Conference
AAPM
Funder
Swedish National Infrastructure for Computing (SNIC), 2014/1-260
Available from: 2015-01-23 Created: 2015-01-23 Last updated: 2018-06-07
Häggström, I., Schmidtlein, C. R., Karlsson, M. & Larsson, A. (2014). Compartment modeling of dynamic brain PET: the impact of scatter corrections on parameter errors. Medical physics, 41(11), 111907
Open this publication in new window or tab >>Compartment modeling of dynamic brain PET: the impact of scatter corrections on parameter errors
2014 (English)In: Medical physics, ISSN 0094-2405, Vol. 41, no 11, p. 111907-Article in journal (Refereed) Published
Abstract [en]

Purpose: The aim of this study was to investigate the effect of scatter and its correction on kinetic parameters in dynamic brain positron emission tomography (PET) tumor imaging. The 2-tissue compartment model was used, and two different reconstruction methods and two scatter correction (SC) schemes were investigated.

Methods: The gate Monte Carlo (MC) softwarewas used to perform 2×15 full PET scan simulations of a voxelized head phantom with inserted tumor regions. The two sets of kinetic parameters of all tissues were chosen to represent the 2-tissue compartment model for the tracer 3′-deoxy- 3′-(18F)fluorothymidine (FLT), and were denoted FLT1 and FLT2. PET data were reconstructed with both 3D filtered back-projection with reprojection (3DRP) and 3D ordered-subset expectation maximization (OSEM). Images including true coincidences with attenuation correction (AC) and true+scattered coincidences with AC and with and without one of two applied SC schemes were reconstructed. Kinetic parameters were estimated by weighted nonlinear least squares fitting of image derived time–activity curves. Calculated parameters were compared to the true input to the MC simulations.

Results: The relative parameter biases for scatter-eliminated data were 15%, 16%, 4%, 30%, 9%, and 7% (FLT1) and 13%, 6%, 1%, 46%, 12%, and 8% (FLT2) for K1, k2, k3, k4,Va, and Ki, respectively. As expected, SC was essential for most parameters since omitting it increased biases by 10 percentage points on average. SC was not found necessary for the estimation of Ki and k3, however. There was no significant difference in parameter biases between the two investigated SC schemes or from parameter biases from scatter-eliminated PET data. Furthermore, neither 3DRP nor OSEM yielded the smallest parameter biases consistently although therewas a slight favor for 3DRP which produced less biased k3 and Ki estimates while OSEM resulted in a less biased Va. The uncertainty in OSEM parameterswas about 26% (FLT1) and 12% (FLT2) larger than for 3DRP although identical postfilters were applied.

Conclusions: SC was important for good parameter estimations. Both investigated SC schemes performed equally well on average and properly corrected for the scattered radiation, without introducing further bias. Furthermore, 3DRP was slightly favorable over OSEM in terms of kinetic parameter biases and SDs.

Place, publisher, year, edition, pages
American Association of Physicists in Medicine, 2014
Keywords
compartment modeling; dynamic pet; monte carlo; scatter correction
National Category
Other Physics Topics Medical Image Processing
Research subject
radiofysik
Identifiers
urn:nbn:se:umu:diva-95115 (URN)10.1118/1.4897610 (DOI)000344999800028 ()
Funder
Swedish National Infrastructure for Computing (SNIC), 2013/1-234
Available from: 2014-10-22 Created: 2014-10-22 Last updated: 2018-06-07Bibliographically approved
Häggström, I. (2014). Quantitative methods for tumor imaging with dynamic PET. (Doctoral dissertation). Umeå: Umeå Universitet
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. p. 94
Series
Umeå University medical dissertations, ISSN 0346-6612 ; 1683
Keywords
Dynamic positron emission tomography, PET, tumor imaging, compartment modeling, Monte Carlo
National Category
Medical Image Processing Other Physics Topics
Research subject
radiofysik
Identifiers
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)
Opponent
Supervisors
Funder
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: 2018-06-07Bibliographically approved
Häggström, I., Schmidtlein, C. R., Karlsson, M. & Larsson, A. (2013). Do scatter and random corrections affect the errors in kinetic parameters in dynamic PET?: a Monte Carlo study. In: 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC): . Paper presented at 60th IEEE Nuclear Science Symposium (NSS) / Medical Imaging Conference (MIC) / 20th International Workshop on Room-Temperature Semiconductor X-ray and Gamma-ray Detectors, Seoul, South Korea, Oct 27-Nov 02, 2013. IEEE conference proceedings
Open this publication in new window or tab >>Do scatter and random corrections affect the errors in kinetic parameters in dynamic PET?: a Monte Carlo study
2013 (English)In: 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), IEEE conference proceedings, 2013, , p. 4Conference paper, Published paper (Refereed)
Abstract [en]

Dynamic positron emission tomography (PET) data can be evaluated by compartmental models, yielding model specific kinetic parameters. For the parameters to be of quantitative use however, understanding and estimation of errors and uncertainties associated with them are crucial.

The aim in this study was to investigate the effects of the inclusion of scattered and random counts and their respective corrections on kinetic parameter errors.

The MC software GATE was used to simulate two dynamic PET scans of a phantom containing three regions; blood, tissue and a static background. The two sets of time-activity-curves (TACs) used were generated for a 2-tissue compartment model with preset parameter values (K1, k2, k3, k4 and Va). The PET data was reconstructed into 19 frames by both ordered-subset expectation maximization (OSEM) and 3D filtered back-projection with reprojection (3DFBPRP) with normalization and additional corrections (A=attenuation, R=random, S=scatter, C=correction): True counts (AC), true+random counts (ARC), true+scattered counts (ASC) and total counts (ARSC).

The results show that parameter estimates from true counts (AC), true+random counts (ARC), true+scattered counts (ASC) and total counts (ARSC) were not significantly different, with the exception of Va where the bias increased with added corrections. Thus, the inclusion of and correction for scattered and random counts did not affect the bias in parameter estimates K1, k2, k3, k4 and Ki. Uncorrected total counts (only AC) resulted in biases of hundreds or even thousands of percent, emphasizing the need for proper corrections. Reconstructions with 3DFBPRP resulted in overall 20-40% less biased estimates compared to OSEM.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2013. p. 4
Keywords
PET, dynamic PET, Monte Carlo, GATE, compartment model, scatter correction, random correction, FLT
National Category
Medical Image Processing
Research subject
radiofysik
Identifiers
urn:nbn:se:umu:diva-98415 (URN)10.1109/NSSMIC.2013.6829388 (DOI)000347163501202 ()978-1-4799-0534-8 (ISBN)
Conference
60th IEEE Nuclear Science Symposium (NSS) / Medical Imaging Conference (MIC) / 20th International Workshop on Room-Temperature Semiconductor X-ray and Gamma-ray Detectors, Seoul, South Korea, Oct 27-Nov 02, 2013
Funder
Swedish National Infrastructure for Computing (SNIC), 2013/1-234Swedish National Infrastructure for Computing (SNIC), HPC2N-2009-001
Available from: 2015-01-22 Created: 2015-01-22 Last updated: 2018-06-07
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-9178-6683

Search in DiVA

Show all publications