Optimization of reconstruction parameters in [18F]FDG PET brain images aiming scan time reduction





PET cerebral, reconstrução, otimização, quantificação, qualidade de imagem, Hoffman


Iterative image reconstruction methods are widely used in PET due to their better image quality when compared to analytical methods. However, inaccurate quantification occurs in low activity concentration regions, which leads to biased quantification of PET images. The diagnosis of some neurodegenerative diseases, such as Alzheimer’s disease, is based on identifying such low-uptake regions. Furthermore, PET imaging in these populations should be as short as possible to limit head movements and improve patient comfort. This work aims to identify optimized reconstruction parameters of [18F]FDG PET brain images aiming to reduce image acquisition time with minimal impact on quantification. For this, [18F]FDG PET images of a Hoffman 3-D brain phantom were acquired. Analytical and iterative reconstruction methods were compared utilizing image quality and quantitative accuracy metrics. OSEM reconstruction algorithm was optimized (4 iterations and 32 subsets). It resulted in remarkably similar images compared to the current clinical settings, with a 50% reduction in scan time (5 min with a post-reconstruction filter of 4 mm). Future clinical studies are needed to confirm the results presented here.


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Saha GB. Basics of PET imaging: physics, chemistry, and regulations. Springer; 2015.

Cherry SR, Sorenson JA, Phelps ME. Physics in nuclear medicine e-Book. Elsevier Health Sciences; 2012.

Chauveau F, Boutin H, Van Camp N, Dollé F, Tavitian B. Nuclear imaging of neuroinflammation: a comprehensive review of [11 C] PK11195 challengers. Eur J Nucl Med Mol Imaging. 2008;35(12):2304–19.

de Araújo AS, Andrade MA, da Silva AMM. Efeito de Volume Parcial na Quantificação de Imagens PET de Indivíduos Idosos Saudáveis. Rev Bras Física Médica. 2018;

Banati RB, Newcombe J, Gunn RN, Cagnin A, Turkheimer F, Heppner F, et al. The peripheral benzodiazepine binding site in the brain in multiple sclerosis. Brain. 2000 Nov;123(11):2321–37.

Jeckel CMM. O uso da Tomografia por Emissão de Pósitrons (pet) no diagnóstico das doenças neurodegenerativas do idoso. PAJAR - Pan Am J Aging Res. 2017 Aug 15;5(1):1.

Shokouhi S, Riddle W, Kang H. A new data analysis approach for measuring longitudinal changes of metabolism in cognitively normal elderly adults. Clin Interv Aging. 2017 Dec;Volume 12:2123–30.

Prando S, Ono CR, Robilotta CC, Sapienza MT. Methods for quantification of cerebral glycolytic metabolism using 2-deoxy-2-[18 F]fluoroglucose in small animals. Res Biomed Eng. 2018 Sep 13;34(3):254–72.

Lim H, Dewaraja YK, Fessler JA. A PET reconstruction formulation that enforces non-negativity in projection space for bias reduction in Y-90 imaging. Phys Med Biol. 2018 Feb 6;63(3):035042.

Jian Y, Planeta B, Carson RE. Evaluation of bias and variance in low-count OSEM list mode reconstruction. Phys Med Biol. 2015 Jan 7;60(1):15–29.

Soret M, Piekarski E, Yeni N, Giron A, Maisonobe J-A, Khalifé M, et al. Dose Reduction in Brain [18F]FDG PET/MRI: Give It Half a Chance. Mol Imaging Biol. 2020 Jun 8;22(3):695–702.

Carlier T, Willowson KP, Fourkal E, Bailey DL, Doss M, Conti M. 90 Y -PET imaging: Exploring limitations and accuracy under conditions of low counts and high random fraction. Med Phys. 2015 Jun 22;42(7):4295–309.

Yan J, Schaefferkoetter J, Conti M, Townsend D. A method to assess image quality for Low-dose PET: analysis of SNR, CNR, bias and image noise. Cancer Imaging. 2016 Dec 26;16(1):26.

Grezes-Besset L, Nuyts J, Boellard R, Buvat I, Michel C, Pierre C, et al. Simulation-based evaluation of NEG-ML iterative reconstruction of low count PET data. In: 2007 IEEE Nuclear Science Symposium Conference Record. IEEE; 2007. p. 3009–14.

Hamdan AC, Bueno OFA. Relações entre controle executivo e memória episódica verbal no comprometimento cognitivo leve e na demência tipo Alzheimer. Estud Psicol. 2005;10(1):63–71.

Krempser AR, de Oliveira SMV, de Almeida SA. Avaliação do efeito de volume parcial na quantificação de atividade em imagens de PET/CT. Rev Bras Física Médica. 2012;6(2):35–40.

Buvat I. Quantification in emission tomography: Challenges, solutions, and performance. Nucl Instruments Methods Phys Res Sect A Accel Spectrometers, Detect Assoc Equip. 2007;571(1–2):10–3.

Boellaard R. Standards for PET Image Acquisition and Quantitative Data Analysis. J Nucl Med. 2009 May 1;50(Suppl_1):11S-20S.

Tong S, Alessio AM, Kinahan PE. Image reconstruction for PET/CT scanners: past achievements and future challenges. Imaging Med [Internet]. 2010 Oct;2(5):529–45. Available from: http://www.futuremedicine.com/doi/abs/10.2217/iim.10.49

Illes J, Rosen A, Greicius M, Racine E. Prospects for Prediction: Ethics Analysis of Neuroimaging in Alzheimer’s Disease. Ann N Y Acad Sci. 2007 Feb 1;1097(1):278–95.

Wardak M, Wong K-P, Shao W, Dahlbom M, Kepe V, Satyamurthy N, et al. Movement Correction Method for Human Brain PET Images: Application to Quantitative Analysis of Dynamic 18F-FDDNP Scans. J Nucl Med. 2010 Feb 1;51(2):210–8.

Waxman, A. D., Herholz, K., Lewis, D. H., Herscovitch, P., Minoshima, S., Mountz, J. M., & Consensus GID. Guideline for FDG PET Brain Imaging. 2009.

ICRP. ICRP Publication 106: Radiation Dose to Patients from Radiopharmaceuticals: A third amendment to ICRP Publication 53. 2008.

Dekaban, Anatole S.; Sadowsky D. Changes in brain weights during the span of human life: relation of brain weights to body heights and body weights. Ann Neurol Off J Am Neurol Assoc Child Neurol Soc. 1978;4(4):345–56.

Barber TW, Brockway JA, Higgins LS. The density of tissues in and about the head. Acta Neurol Scand [Internet]. 1970 Mar;46(1):85–92. Available from: http://doi.wiley.com/10.1111/j.1600-0404.1970.tb05606.x

GE HEALTHCARE. VUE Point HD: Bringing accuracy to PET reconstruction. Waukesha; 2008.

Leemans EL, Kotasidis F, Wissmeyer M, Garibotto V, Zaidi H. Qualitative and Quantitative Evaluation of Blob-Based Time-of-Flight PET Image Reconstruction in Hybrid Brain PET/MR Imaging. Mol Imaging Biol. 2015 Oct 30;17(5):704–13.

Habert M-O, Marie S, Bertin H, Reynal M, Martini J-B, Diallo M, et al. Optimization of brain PET imaging for a multicentre trial: the French CATI experience. EJNMMI Phys. 2016 Dec 5;3(1):6.

Schiller F, Frings L, Thurow J, Meyer PT, Mix M. Limits for Reduction of Acquisition Time and Administered Activity in 18 F-FDG PET Studies of Alzheimer Dementia and Frontotemporal Dementia. J Nucl Med. 2019 Dec;60(12):1764–70.

Shkumat NA, Vali R, Shammas A. Clinical evaluation of reconstruction and acquisition time for pediatric 18F-FDG brain PET using digital PET/CT. Pediatr Radiol. 2020 Jun 3;50(7):966–72.

Fällmar D, Lilja J, Kilander L, Danfors T, Lubberink M, Larsson E-M, et al. Validation of true low-dose 18F-FDG PET of the brain. Am J Nucl Med Mol Imaging. 2016;6(5):269–76.

Shan ZY, Leiker AJ, Onar-Thomas A, Li Y, Feng T, Reddick WE, et al. Cerebral glucose metabolism on positron emission tomography of children. Hum Brain Mapp. 2014 May;35(5):2297–309.

Caribé PRR V., Koole M, D’Asseler Y, Van Den Broeck B, Vandenberghe S. Noise reduction using a Bayesian penalized-likelihood reconstruction algorithm on a time-of-flight PET-CT scanner. EJNMMI Phys. 2019 Dec 10;6(1):22.

Caribé PRR V., Koole M, D’Asseler Y, Deller TW, Van Laere K, Vandenberghe S. NEMA NU 2–2007 performance characteristics of GE Signa integrated PET/MR for different PET isotopes. EJNMMI Phys. 2019 Dec 4;6(1):11.




Como Citar

Pinto, S. O., Caribe, P. R. R. V., Narciso, L., & Marques da Silva, A. M. (2021). Optimization of reconstruction parameters in [18F]FDG PET brain images aiming scan time reduction. Revista Brasileira De Física Médica, 15, 611. https://doi.org/10.29384/rbfm.2021.v15.19849001611



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