umu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Quantitative methods for tumor imaging with dynamic PET
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
2014 (English)Doctoral thesis, comprehensive summary (Other academic)Alternative title
Kvantitativa metoder för tumöravbildning med dynamisk PET (Swedish)
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.
Series
Umeå University medical dissertations, ISSN 0346-6612 ; 1683
Keyword [en]
Dynamic positron emission tomography, PET, tumor imaging, compartment modeling, Monte Carlo
National Category
Medical Image Processing Other Physics Topics
Research subject
radiofysik
Identifiers
URN: urn:nbn:se:umu:diva-95126ISBN: 978-91-7601-160-7 (print)OAI: oai:DiVA.org:umu-95126DiVA: diva2:761373
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: 2016-05-26Bibliographically approved
List of papers
1. Semi-automatic tumour segmentation by selective navigation in a three-parameter volume, obtained by voxel-wise kinetic modelling of 11C-acetate
Open this publication in new window or tab >>Semi-automatic tumour segmentation by selective navigation in a three-parameter volume, obtained by voxel-wise kinetic modelling of 11C-acetate
Show others...
2010 (English)In: Radiation Protection Dosimetry, ISSN 0144-8420, E-ISSN 1742-3406, Vol. 139, no 1-3, 214-218 p.Article in journal (Refereed) Published
Abstract [en]

Positron emission tomography (PET) is increasingly used for delineation of tumour tissue in, for example, radiotherapy treatment planning. The most common method used is to outline volumes with a certain per cent uptake over background in a static image. However, PET data can also be collected dynamically and analysed by kinetic models, which potentially represent the underlying biology better. In the present study, a three-parameter kinetic model was used for voxel-wise evaluation of (11)C-acetate data of head/neck tumours. These parameters which represent the tumour blood volume, the uptake rate and the clearance rate of the tissue were derived for each voxel using a linear regression method and used for segmentation of active tumour tissue. This feasibility study shows that it is possible to segment images based on derived model parameters. There is, however, room for improvements concerning the PET data acquisition, noise reduction and the kinetic modelling. In conclusion, this early study indicates a strong potential of the method even though no 'true' tumour volume was available for validation.

Place, publisher, year, edition, pages
Oxford University Press, 2010
National Category
Medical Image Processing
Research subject
radiofysik
Identifiers
urn:nbn:se:umu:diva-34228 (URN)10.1093/rpd/ncq075 (DOI)000277738200038 ()20200103 (PubMedID)
Available from: 2010-05-21 Created: 2010-05-21 Last updated: 2017-12-12Bibliographically approved
2. Compartment modeling of dynamic brain PET: the impact of scatter corrections on parameter errors
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, 111907- p.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
Keyword
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: 2016-05-26Bibliographically approved
3. A Monte Carlo study of the dependence of early frame sampling on uncertainty and bias in pharmacokinetic parameters from dynamic PET
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, 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

Keyword
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: 2017-12-05Bibliographically approved
4. PETSTEP: generation of synthetic PET lesions for fast evaluation of segmentation methods
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, 969-980 p.Article 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.

Keyword
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: 2017-04-20Bibliographically approved

Open Access in DiVA

Avhandling(8203 kB)1157 downloads
File information
File name FULLTEXT01.pdfFile size 8203 kBChecksum SHA-512
b7b7c57704f6510905b2858e6fd1aa29b7d81be9056842d7e296220ae6ddcb2e5029f86e856deced679540bc1377497967abcad24c7010dfc980834373ec7521
Type fulltextMimetype application/pdf

Authority records BETA

Häggström, Ida

Search in DiVA

By author/editor
Häggström, Ida
By organisation
Radiation Physics
Medical Image ProcessingOther Physics Topics

Search outside of DiVA

GoogleGoogle Scholar
Total: 1157 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 1353 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf