Skip to main content
SpringerLink
Log in

Image Motion Correction of GATE Simulation in Dedicated PET Scanner with Open Geometry

  • Conference paper
  • First Online:
Book cover Bioengineering and Biomedical Signal and Image Processing (BIOMESIP 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12940))

  • 677 Accesses

Abstract

Positron Emission Tomography (PET) images are considerably degraded by respiratory and involuntary motions of the patient inside the scanner, having a direct effect in a misdiagnosis. In this paper, a dedicated PET scanner with an open geometry is proposed. This PET configuration poses several challenges to image reconstruction, such as limited angles, motion correction and the sensitivity correction problem. The paper presents a GATE simulation study of image motion correction using XCAT phantom using a multi-frame algorithm called Enhance Multiple Acquisition Frames (EMAF) to correct rigid body and respiratory motion with list-mode data using time of flight (TOF) information and patient motion. This approach is implemented in three phases: frames cutting, image reconstruction and finally image registration. Additionally, the information provided by the TOF is used to improve the reconstruction due to the lack of angular information provided by the proposed open geometry system. Two performance tests are applied to validate the results, obtaining a remarkable resolution improvement after being processed. The peak signal noise ratio (PSNR) values for the corrected and uncorrected images are, respectively, 30 versus 28 dB, and for the image matching precision (IMP), 89% versus 78%. The obtained results show that the method improves the signal intensity over the background in comparison with other literature methods, maximizing the similarity between the ground-truth (static) image and the corrected image and minimizing the intra-frame motion.

Supported by the Spanish Government Grants TEC2016-79884-C2, PID2019-107790RB-C22, and PEJ2018-002230-A-AR; the Generalitat Valenciana GJIDI/2018/A/040l and the PTA2019-017113-1/AEI/10.13039/501100011033; the European Union through the European Regional Development Fund (ERDF); and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant Agreement No. 695536).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. González, A.J., Sánchez, F., Benlloch, J.M.: Organ-dedicated molecular imaging systems. In: IEEE Transactions on Radiation and Plasma Medical Sciences, vol. 2, no. 5, pp. 388–403, September 2018. https://doi.org/10.1109/TRPMS.2018.2846745

  2. Vaquero, J.J., Kinahan, P.: Positron emission tomography: current challenges and opportunities for technological advances in clinical and preclinical imaging systems. Ann. Rev. Biomed. Eng. 17, 385–414 (2015). https://doi.org/10.1146/annurev-bioeng-071114-040723

    Article  Google Scholar 

  3. Abreu, M.C., et al.: Design and evaluation of the clear-PEM scanner for positron emission mammography. IEEE Trans. Nucl. Sci. 53(1), 71–77 (2006). https://doi.org/10.1109/TNS.2006.870173

    Article  MathSciNet  Google Scholar 

  4. Moliner, L., et al.: Design and evaluation of the MAMMI dedicated breast PET. Med. Phys. 39(9), 5393–5404 (2012). https://doi.org/10.1118/1.4742850

    Article  Google Scholar 

  5. Cañizares, G., Gonzalez-Montoro, A., Freire, M., et al.: Pilot performance of a dedicated prostate PET suitable for diagnosis and biopsy guidance. EJNMMI Phys. 7, 38 (2020). https://doi.org/10.1186/s40658-020-00305-y

    Article  Google Scholar 

  6. Oliver, S., Moliner, L., Ilisie, V., Benlloch, J.M., Rodríguez-Álvarez, M.J.: Simulation study for designing a dedicated cardiac TOF-PET system. Sens. (Basel, Switz.) 20(5), 1311 (2020). https://doi.org/10.3390/s20051311

    Article  Google Scholar 

  7. Jan, S., Santin, G., Strul, D., et al.: GATE: a simulation toolkit for PET and SPECT. Phys. Med. Biol. 49(19), 4543–4561 (2004). https://doi.org/10.1088/0031-9155/49/19/007

    Article  Google Scholar 

  8. Jan, S., Benoit, D., Becheva, E., et al.: GATE V6: a major enhancement of the GATE simulation platform enabling modelling of CT and radiotherapy. Phys. Med. Biol. 56(4), 881–901 (2011). https://doi.org/10.1088/0031-9155/56/4/001

    Article  Google Scholar 

  9. Sarrut, D., Bardiès, M., Boussion, N., et al.: A review of the use and potential of the GATE Monte Carlo simulation code for radiation therapy and dosimetry applications. Med. Phys. 41(6), 064301 (2014). https://doi.org/10.1118/1.4871617

  10. Segars, W.P., Sturgeon, G., Mendonca, S., Grimes, J., Tsui, B.M.: 4D XCAT phantom for multimodality imaging research. Med. Phys. 37(9), 4902–4915 (2010). https://doi.org/10.1118/1.3480985

    Article  Google Scholar 

  11. Chan, C., et al.: Non-rigid event-by-event continuous respiratory motion compensated list-mode reconstruction for PET. IEEE Trans. Med. Imaging 37(2), 504–515 (2018). https://doi.org/10.1109/TMI.2017.2761756

    Article  Google Scholar 

  12. Cañizares, G., et al.: Motion correction of multi-frame PET data. IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), pp. 1–4 (2019)

    Google Scholar 

  13. Picard, Y., Thompson, C.J.: Motion correction of PET images using multiple acquisition frames. IEEE Trans. Med. Imaging 16, 137–144 (1997). https://doi.org/10.1109/NSS/MIC42101.2019.9059930.Picard

    Article  Google Scholar 

  14. Espinós-Morató, H., Cascales-Picó, D., Vergara, M., Hernández-Martínez, Á., Benlloch Baviera, J.M., Rodríguez-Álvarez, M.J.: Simulation study of a frame-based motion correction algorithm for positron emission imaging. Sensors 21(8), 2608 (2021). https://doi.org/10.3390/s21082608

    Article  Google Scholar 

  15. Shepp, L.A., Vardi, Y.: Maximum likelihood reconstruction for emission tomography. IEEE Trans. Med. Imaging 1(2), 113–122 (1982). https://doi.org/10.1109/TMI.1982.4307558

    Article  Google Scholar 

  16. McCormick, M., Liu, X., Jomier, J., Marion, C., Ibanez, L.: ITK: enabling reproducible research and open science. Front. Neuroinf. 2014(8), 13 (2002). https://doi.org/10.3389/fninf.2014.00013

    Article  Google Scholar 

  17. Yoo, T.S., et al.: Engineering and algorithm design for an image processing API: a technical report on ITK – the insight toolkit. In: Westwood, J. (eds.) Proceedings of Medicine Meets Virtual Reality, pp. 586–592. IOS Press Amsterdam (2002)

    Google Scholar 

  18. Bai, W., Brady, S.M.: Spatio-temporal image registration for respiratory motion correction in pet imaging. In: Proceedings of the 2009 IEEE International Symposium on Biomedical Imaging: from Nano to Macro, Boston, MA, USA, 28 June–1 July 2009; ISBI 2009, pp. 426–429. IEEE, New York, (2009)

    Google Scholar 

  19. Gigengack, F., Ruthotto, L., Burger, M., Wolters, C.H., Jiang, X., Schafers, K.P.: Motion correction in dual gated cardiac PET using mass-preserving image registration. IEEE Trans. Med. Imaging 31, 698–712 (2012)

    Article  Google Scholar 

  20. Arsigny, V., Fillard, P., Pennec, X., Ayache, N.: Fast and simple calculus on tensors in the log-euclidean framework. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 115–122. Springer, Heidelberg (2005). https://doi.org/10.1007/11566465_15

    Chapter  Google Scholar 

  21. Neumaier, A.: Solving ill-conditioned and singular linear systems: a tutorial on regularization. SIAM Rev. 40, 636–666 (1998)

    Article  MathSciNet  Google Scholar 

  22. Burger, M., Modersitzki, J., Ruthotto, L.: A hyperelastic regularization energy for image registration. SIAM J. Sci. Comput. 35, B132–B148 (2013)

    Article  MathSciNet  Google Scholar 

  23. Pennec, X.., Stefanescu, R.., Arsigny, V.., Fillard, P.., Ayache, N..: Riemannian elasticity: a statistical regularization framework for non-linear registration. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3750, pp. 943–950. Springer, Heidelberg (2005). https://doi.org/10.1007/11566489_116

    Chapter  Google Scholar 

Download references

Acknowledgments

This research has been supported by the Spanish Government Grants TEC2016-79884-C2, PID2019-107790RB-C22, and PEJ2018-002230-A-AR; the Generalitat Valenciana GJIDI/2018/A/040l and the PTA2019-017113-1/AEI/10.13039/501100011033; the European Union through the European Regional Development Fund (ERDF); and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant Agreement No. 695536).

Author information

Authors and Affiliations

  1. Instituto de Instrumentación para Imagen Molecular (i3M), Universitat Politècnica de València (UPV) - Consejo Superior de Investigaciones Científicas (CSIC), 46022, Valencia, Spain

    Héctor Espinós-Morató, David Cascales-Picó, Marina Vergara & María José Rodríguez-Álvarez

  2. Department of Imaging and Pathology, Division of Nuclear Medicine, KU Leuven, 3000, Leuven, Belgium

    Marina Vergara

Authors
  1. Héctor Espinós-Morató
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David Cascales-Picó
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marina Vergara
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. María José Rodríguez-Álvarez
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: H.E-M. and M.J.R-Á.; methodology: H.E-M.; software: H.E-M., M.V., and D.C-P.; validation: H.E-M. and D.C-P.; formal analysis: H.E-M., D.C-P., and M.V.; investigation, H.E-M., D.C-P., M.V., and Á.H-M.; resources: H.E-M.; data curation, H.E-M., M.V., and D.C-P.; framing: D.C-P.; reconstruction: M.V.; registration: Á.H-M. and H.E-M.; writing—original draft preparation: H.E-M.; writing—review and editing: D.C-P., M.V., Á.H-M., and M.J.R-Á.; visualization: D.C-P. and H.E-M.; supervision: M.J.R-Á.; project administration: M.J.R-Á.; funding acquisition: M.J.R-Á.; J.M.B.B. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Héctor Espinós-Morató .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Granada, Granada, Spain

    Daniel Castillo-Secilla

  3. University of Granada, Granada, Spain

    Luis Javier Herrera

  4. Universidad de Granada, Granada, Granada, Spain

    Héctor Pomares

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Espinós-Morató, H., Cascales-Picó, D., Vergara, M., Rodríguez-Álvarez, M.J. (2021). Image Motion Correction of GATE Simulation in Dedicated PET Scanner with Open Geometry. In: Rojas, I., Castillo-Secilla, D., Herrera, L.J., Pomares, H. (eds) Bioengineering and Biomedical Signal and Image Processing. BIOMESIP 2021. Lecture Notes in Computer Science(), vol 12940. Springer, Cham. https://doi.org/10.1007/978-3-030-88163-4_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-88163-4_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88162-7

  • Online ISBN: 978-3-030-88163-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation