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Quantitative Monte Carlo-based brain dopamine transporter SPECT imaging

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Abstract

Objective

Brain dopamine transporter imaging with I-123-labeled radioligands is technically demanding due to the small size of the imaging target relative to the spatial resolution of most SPECT systems. In addition, I-123 has high-energy peaks which can penetrate or scatter in the collimator and be detected in the imaging energy window. The aim of this study was to implement Monte Carlo (MC)-based full collimator–detector response (CDR) compensation algorithm for I-123 into a third-party commercial SPECT reconstruction software package and to evaluate its effect on the quantitative accuracy of dopaminergic-image analysis compared to a method where only the geometric component of the CDR is compensated.

Methods

In this work, we utilized a full Monte Carlo collimator–detector model and incorporated it into an iterative SPECT reconstruction algorithm. The full Monte Carlo model reconstruction was compared to standard reconstruction using an anthropomorphic striatal phantom filled with different I-123 striatal/cortex uptake ratios and with clinical I-123 Ioflupane DaTScan studies.

Results

Reconstruction with the full model yielded higher (13–25%) striatal uptake ratios than the conventional reconstruction, but the uptake ratios were still much lower than the true ratios due to partial volume effect. Visually, images reconstructed with the full Monte Carlo model had better contrast and resolution than the conventional images, with both phantom and patient studies.

Conclusions

Reconstruction with full Monte Carlo collimator–detector model yields higher quantitative accuracy than conventional reconstruction. Additional work to reduce the partial volume effect related errors would improve the accuracy further.

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References

  1. Eerola J, Tienari PJ, Kaakkola S, Nikkinen P, Launes J. How useful is [123I]beta-CIT SPECT in clinical practice? J Neurol Neurosurg Psychiatry. 2005;76:1211–6.

    Article  CAS  Google Scholar 

  2. Kish SJ, Shannak K, Hornykiewicz O. Uneven pattern of dopamine loss in the striatum of patients with idiopathic Parkinson’s disease: pathophysiologic and clinical implications. N Engl J Med. 1988;318:876–80.

    Article  CAS  Google Scholar 

  3. Jaber M, Jones S, Giros B, Caron MG. The dopamine transporter: a crucial component regulating dopamine transmission. Mov Disord. 1997;12:629–33.

    Article  CAS  Google Scholar 

  4. Djang DSW, Janssen MJR, Bohnen N, Booij J, Henderson TA, Herholz K, et al. SNM practice guideline for dopamine transporter imaging with 123I-ioflupane SPECT 1.0. J Nucl Med. 2012;53:154–63.

    Article  CAS  Google Scholar 

  5. Darcourt J, Booij J, Tatsch K, Varrone A, Vander Borght T, Kapucu OL, et al. EANM procedure guidelines for brain neurotransmission SPECT using (123) I-labelled dopamine transporter ligands, version 2. Eur J Nucl Med Mol Imaging. 2010;37(2):443–50.

    Article  CAS  Google Scholar 

  6. Du Y, Tsui BMW, Frey EC. Model-based compensation for quantitative 123I brain SPECT imaging. Phys Med Biol. 2006;51:1269–82.

    Article  Google Scholar 

  7. Pareto D, Cot A, Pavía J, Falcón C, Juvells I, Lomeña F, et al. Iterative reconstruction with correction of the spatially variant fanbeam collimator response in neurotransmission SPET imaging. Eur J Nucl Med Mol Imaging. 2003;30:1322–9.

    Article  Google Scholar 

  8. Soret M, Koulibaly PM, Darcourt J, Hapdey S, Buvat I. Quantitative accuracy of dopaminergic neurotransmission imaging with 123I SPECT. J Nucl Med. 2003;44:1184–93.

    CAS  PubMed  Google Scholar 

  9. Catafau AM. Brain SPECT of dopaminergic neurotransmission: a new tool with proved clinical impact. Nucl Med Commun. 2001;22:1059–60.

    Article  CAS  Google Scholar 

  10. Hutton BF, Buvat I, Beekman FJ. Review and current status of SPECT scatter correction. Phys Med Biol. 2011;56:R85–R112.

    Article  Google Scholar 

  11. Bailey DL, Willowson KP. An evidence-based review of quantitative spect imaging and potential clinical applications. J Nucl Med. 2013;54:83–9.

    Article  Google Scholar 

  12. Sohlberg A, Watabe H, Iida H. Acceleration of Monte Carlo-based scatter compensation for cardiac SPECT. Phys Med Biol. 2008;21:N277–285.

    Article  Google Scholar 

  13. Hudson M, Larkin RS. Accelerated image reconstruction using ordered subsets of projection data. IEEE Trans Med Imaging. 1994;13:601–9.

    Article  CAS  Google Scholar 

  14. Di Bella EVR, Barclay AB, Eisner RL, Schafer RW. A comparison of rotation-based methods for iterative reconstruction algorithms. IEEE Trans Nucl Sci. 1996;43(6):3370–6.

    Article  Google Scholar 

  15. de Jong H, Slijpen E, Beekman F. Acceleration of Monte Carlo SPECT simulation using convolution based forced detection. IEEE Trans Nucl Sci. 2001;48:58–64.

    Article  Google Scholar 

  16. Sohlberg A, Kajaste M. Fast Monte Carlo-simulator with full collimator and detector response modelling for SPECT. Ann Nucl Med. 2012;26:92–8.

    Article  Google Scholar 

  17. Kangasmaa TS, Constable C, Sohlberg AO. Evaluation of quantitative 123I and 131I SPECT with Monte Carlo-based down-scatter compensation. Nucl Med Commun. 2018;39:1097–102.

    Article  Google Scholar 

  18. Koch W, Radau PE, Hamann C, Tatsch K. Clinical testing of an optimized software solution for an automated, observer independent evaluation of dopamine transporter SPECT studies. J Nucl Med. 2005;46:1109–18.

    PubMed  Google Scholar 

  19. Bexelius T, Sohlberg A. Implementation of GPU accelerated SPECT reconstruction with Monte Carlo-based scatter correction. Ann Nucl Med. 2018;32:337–47.

    Article  CAS  Google Scholar 

  20. Tossici-Bolt L, Dickson JC, Sera T, Booij J, Asenbaun-Nan S, Bagnara MC, et al. [123I]FP-CIT ENC-DAT normal database: the impact of the reconstruction and quantification methods. EJNMMI Physics. 2017;4:8.

    Article  Google Scholar 

  21. Crespo C, Gallego J, Cot A, Falcón C, Bullich S, Pareto D, et al. Quantification of dopaminergic neurotransmission SPECT studies with 123I-labelled radioligands. A comparison between different imaging systems and data acquisition protocols using Monte Carlo simulation. Eur J Nucl Med Mol Imaging. 2008;35:1334–422.

    Article  CAS  Google Scholar 

  22. Thomas BA, Cuplov V, Bousse A, Mendes A, Thielemans K, Hutton BF, et al. PETPVC: a toolbox for performing partial volume correction techniques in positron emission tomography. Phys Med Biol. 2016;61:7975–93.

    Article  Google Scholar 

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Correspondence to Tuija Kangasmaa.

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Kangasmaa, T., Hippeläinen, E., Constable, C. et al. Quantitative Monte Carlo-based brain dopamine transporter SPECT imaging. Ann Nucl Med 35, 17–23 (2021). https://doi.org/10.1007/s12149-020-01532-0

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  • DOI: https://doi.org/10.1007/s12149-020-01532-0

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