Skip to main content
SpringerLink
Log in

A sinogram warping strategy for pre-reconstruction 4D PET optimization

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

A novel strategy for 4D PET optimization in the sinogram domain is proposed, aiming at motion model application before image reconstruction (“sinogram warping” strategy). Compared to state-of-the-art 4D-MLEM reconstruction, the proposed strategy is able to optimize the image SNR, avoiding iterative direct and inverse warping procedures, which are typical of the 4D-MLEM algorithm. A full-count statistics sinogram of the motion-compensated 4D PET reference phase is generated by warping the sinograms corresponding to the different PET phases. This is achieved relying on a motion model expressed in the sinogram domain. The strategy was tested on the anthropomorphic 4D PET–CT NCAT phantom in comparison with the 4D-MLEM algorithm, with particular reference to robustness to PET–CT co-registrations artefacts. The MLEM reconstruction of the warped sinogram according to the proposed strategy exhibited better accuracy (up to +40.90 % with respect to the ideal value), whereas images reconstructed according to the 4D-MLEM reconstruction resulted in less noisy (down to −26.90 % with respect to the ideal value) but more blurred. The sinogram warping strategy demonstrates advantages with respect to 4D-MLEM algorithm. These advantages are paid back by introducing approximation of the deformation field, and further efforts are required to mitigate the impact of such an approximation in clinical 4D PET reconstruction.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Chang T, Chang G, Clark JW Jr, Diab RH, Rohren E, Mawlawi OR (2012) Reliability of predicting image signal-to-noise ratio using noise equivalent count rate in PET imaging. Med Phys 39(10):5891–5900

    Article  PubMed  PubMed Central  Google Scholar 

  2. Christensen GE, Johnson HJ (2001) Consistent image registration. IEEE Trans Med Imaging 20(7):568–582

    Article  CAS  PubMed  Google Scholar 

  3. Cirilli M, Dosanjh M (2014) Education and training in medical imaging for conventional and particle radiation therapy through the EC founded ENVISION and ENTERVISION. Rom Rep Phys 66(1):22–29

    Google Scholar 

  4. Conti M, Bendriem B, Casey M, Chen M, Kehren F, Michel C, Panin V (2005) First experimental results of time-of-flight reconstruction on an LSO PET scanner. Phys Med Biol 50(19):4507

    Article  PubMed  Google Scholar 

  5. Gianoli C, Bauer J, Kurz C, Riboldi M, Parodi K, Baroni G (2013) A clinical study on 4D pet optimization and quantification in off-line treatment verification. Particle Therapy Co-Operative Group (PTCOG), Essen

    Google Scholar 

  6. Gianoli C, Kurz C, Riboldi M, Bauer J, Baroni G, Debus J, Parodi K (2013) Motion compensated reconstructions in 4D PET-based ion beam treatment verification. In: 4D treatment planning workshop, PSI, Villingen

  7. Gianoli C, Riboldi M, Fontana G, Giri MG, Grigolato D, Ferdeghini M, Cavedon C, Baroni G (2015) Optimized PET imaging for 4D treatment planning in radiotherapy: the virtual 4D PET strategy. Technol Cancer Res Treat 14(1):99–110

    Article  PubMed  Google Scholar 

  8. Gianoli C, Kurz C, Riboldi M, Bauer J, Baroni G, Parodi K (2014) Motion compensated reconstructions in PET-based ion beam treatment verification for moving target. In: International conference on translational research in radio-oncology and physics for health in Europe (ICTR-PHE), Geneva

  9. Gianoli C, Riboldi M, Kurz C, Parodi K, Baroni G (2014) A sinogram warping strategy for pre-reconstruction 4D PET optimization in ion beam therapy application. Radiother Oncol 111:S108

    Article  Google Scholar 

  10. Gianoli C, Riboldi M, Kurz C, De Bernardi E, Bauer J, Fontana G, Ciocca M, Parodi K, Baroni G (2014) PET–CT scanner characterization for PET raw data use in biomedical research. Comput Med Imaging Graph 38(5):358–368

    Article  PubMed  Google Scholar 

  11. Gianoli C, Bauer J, Riboldi M, De Bernardi E, Fattori G, Baselli G, Debus J, Parodi K, Baroni G (2014) Regional MLEM reconstruction strategy for PET-based treatment verification in ion beam radiotherapy. Phys Med Biol 59(22):6979

    Article  PubMed  Google Scholar 

  12. Jan S, Santin G, Strul D, Staelens S, Assié K, Autret D, Avner S, Barbier R, Bardiès M, Bloomfield PM, Brasse D, Breton V, Bruyndonckx P, Buvat I, Chatziioannou AF, Choi Y, Chung YH, Comtat C, Donnarieix D, Ferrer L, Glick SJ, Groiselle CJ, Guez D, Honore P-F, Kerhoas-Cavata S, Kirov AS, Kohli V, Koole M, Krieguer M, van der Laan DJ, Lamare F, Largeron G, Lartizien C, Lazaro D, Maas MC, Maigne L, Mayet F, Melot F, Merheb C, Pennacchio E, Perez J, Pietrzyk U, Rannou FR, Rey M, Schaart DR, Schmidtlein CR, Simon L, Song TY, Vieira J-M, Visvikis D, Van de Walle R, Wieërs E, Morel C (2004) GATE: a simulation toolkit for PET and SPECT. Phys Med Biol 49(19):4543

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Knopf A, Nill S, Yohannes I, Graeff C, Dowdell S, Kurz C, Sonke J-J, Biegun AK, Lang S, McClelland J, Champion B, Fast M, Wölfelschneider J, Gianoli C, Rucinski A, Baroni G, Richter C, van de Water S, Grassberger C, Weber D, Per Poulsen S, Shimizu C (2014) Challenges of radiotherapy: report on the 4D treatment planning workshop 2013. Phys Med 30(7):809–815

    Article  PubMed  Google Scholar 

  14. Lamare F, Carbayo ML, Cresson T, Kontaxakis G, Santos A, Le Rest CC, Reader AJ, Visvikis D (2007) List-mode-based reconstruction for respiratory motion correction in PET using non-rigid body transformations. Phys Med Biol 52(17):5187

    Article  CAS  PubMed  Google Scholar 

  15. Laube K, Menkel S, Bert C, Enghardt W, Helmbrecht S, Saito N, Fiedler F (2013) 4D particle therapy PET simulation for moving targets irradiated with scanned ion beams. Phys Med Biol 58(3):513–533

    Article  CAS  PubMed  Google Scholar 

  16. Lee JS, Kim B, Chee Y, Kwark C, Lee MC, Park KS (2000) Fusion of coregistered cross-modality images using a temporally alternating display method. Med Biol Eng Comput 38(2):127–132

    Article  CAS  PubMed  Google Scholar 

  17. Lewitt RM, Matej S (2003) Overview of methods for image reconstruction from projections in emission computed tomography. Proc IEEE 91(10):1588–1611

    Article  Google Scholar 

  18. Li T, Thorndyke B, Schreibmann E, Yang Y, Xing L (2006) Model-based image reconstruction for four-dimensional PET. Med Phys 33(5):1288–1298

    Article  PubMed  Google Scholar 

  19. McClelland JR, Hawkes DJ, Schaeffter T, King AP (2013) Respiratory motion models: a review. Med Image Anal 17(1):19–42

    Article  CAS  PubMed  Google Scholar 

  20. Nehmeh SA (2013) Respiratory motion correction strategies in thoracic PET–CT imaging. PET Clin 8(1):29–36

    Article  Google Scholar 

  21. Parodi K, Saito N, Chaudhri N, Richter C, Durante M, Enghardt W, Rietzel E, Bert C (2009) 4D in-beam positron emission tomography for verification of motion-compensated ion beam therapy. Med Phys 36(9):4230–4243

    Article  CAS  PubMed  Google Scholar 

  22. Rahmim A, Rousset O, Zaidi H (2007) Strategies for motion tracking and correction in PET. PET Clin 2(2):251–266

    Article  Google Scholar 

  23. Rahmim A, Tang J, Zaidi H (2009) Four-dimensional (4D) image reconstruction strategies in dynamic PET: beyond conventional independent frame reconstruction. Med Phys 36(8):3654–3670

    Article  PubMed  Google Scholar 

  24. Rahmim A, Tang J, Zaidi H (2013) Four-dimensional image reconstruction strategies in cardiac-gated and respiratory-gated PET imaging. PET Clin 8(1):51–67

    Article  Google Scholar 

  25. Reader AJ, Zaidi H (2007) Advances in PET image reconstruction. PET Clin 2(2):173–190

    Article  Google Scholar 

  26. Segars WP (2001) Development of a new dynamic NURBS-based cardiac torso (NCAT) phantom. Ph.D. thesis, University of North Carolina

  27. Shackleford JA, Kandasamy N, Sharp GC (2010) On developing B-spline registration algorithms for multi-core processors. Phys Med Biol 55(21):6329

    Article  CAS  PubMed  Google Scholar 

  28. Strother SC, Casey ME, Hoffman EJ (1990) Measuring PET scanner sensitivity: relating countrates to image signal-to-noise ratios using noise equivalents counts. IEEE Trans Nucl Sci 37(2):783–788

    Article  Google Scholar 

  29. Stützer K, Bert C, Enghardt W, Helmbrecht S, Parodi K, Priegnitz M, Saito N, Fiedler F (2013) Experimental verification of a 4D MLEM reconstruction algorithm used for in-beam PET measurements in particle therapy. Phys Med Biol 58(15):5085–5111

    Article  PubMed  Google Scholar 

  30. Surti S, Kuhn A, Werner ME, Perkins AE, Kolthammer J, Karp JS (2007) Performance of Philips Gemini TF PET/CT scanner with special consideration for its time-of-flight imaging capabilities. J Nucl Med 48(3):471–480

    PubMed  Google Scholar 

  31. Wang J, Del Valle M, Goryawala M, Franquiz JM, Mcgoron AJ (2010) Computer-assisted quantification of lung tumors in respiratory gated PET/CT images: phantom study. Med Biol Eng Comput 48(1):49–58

    Article  CAS  PubMed  Google Scholar 

  32. Woo SK, Watabe H, Choi Y, Kim KM, Park CC, Bloomfield PM, Iida H (2004) Sinogram-based motion correction of pet images using optical motion tracking system and list-mode data acquisition. IEEE Trans Nucl Sci 51(3):782–788

    Article  Google Scholar 

  33. Yang D, Li H, Low DA, Deasy JO, El Naqa I (2008) A fast inverse consistent deformable image registration method based on symmetric optical flow computation. Phys Med Biol 53(21):6143

    Article  PubMed  PubMed Central  Google Scholar 

  34. Zhu H, Shu H, Zhou J, Toumoulin C, Luo L (2006) Image reconstruction for positron emission tomography using fuzzy nonlinear anisotropic diffusion penalty. Med Biol Eng Comput 44(11):983–997

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

This work was partially supported by the ENVISION EU FP7 program and the ULICE EU FP7 program.

Author information

Authors and Affiliations

  1. Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy

    Chiara Gianoli, Marco Riboldi & Guido Baroni

  2. Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany

    Chiara Gianoli, Christopher Kurz & Katia Parodi

  3. Department of Experimental Physics, Medical Physics and Department of Radiation Therapy, Ludwig Maximilian University, Munich, Am Coulombwall 1, 85748, Garching, Germany

    Chiara Gianoli, Christopher Kurz & Katia Parodi

  4. Bioengineering Unit, National Center for Oncologic Hadrontherapy (CNAO), Strada Privata Campeggi 53, 27100, Pavia, Italy

    Marco Riboldi, Giulia Fontana & Guido Baroni

  5. Heidelberg Ion Beam Therapy Center, Im Neuenheimer Feld 450, 69120, Heidelberg, Germany

    Christopher Kurz & Katia Parodi

Authors
  1. Chiara Gianoli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marco Riboldi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Giulia Fontana
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Christopher Kurz
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Katia Parodi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Guido Baroni
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chiara Gianoli.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gianoli, C., Riboldi, M., Fontana, G. et al. A sinogram warping strategy for pre-reconstruction 4D PET optimization. Med Biol Eng Comput 54, 535–546 (2016). https://doi.org/10.1007/s11517-015-1339-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-015-1339-y

Keywords

Search

Navigation