Skip to main content
SpringerLink
Log in

Marker-less and calibration-less motion correction method for brain PET

  • Research Article
  • Published:
Radiological Physics and Technology Aims and scope Submit manuscript

Abstract

Marker-less head motion correction methods have been well-studied; however, no reports discussing potential issues in positional calibration between a PET system and an external sensor remain limited. In this study, we develop a method for positional calibration between the PET system and an external range sensor to achieve practical head motion correction. The basic concept of the developed method involves using the subject’s face model as a marker not only for head motion detection but also for the system positional calibration. The face model of the subject, which can be obtained easily using the range sensor, can also be calculated from a computed tomography (CT) image of the same subject. The CT image, which is acquired separately for attenuation correction in PET, has the same coordinates as the PET image because of the appropriate matching algorithm between CT and PET images. The proposed method was implemented in the helmet-type PET and the motion correction accuracy was assessed quantitatively using a mannequin head. The phantom experiments demonstrated the performance of the developed motion correction method; high-resolution images with no trace of the applied motion were obtained as if no motion was provided. Statistical analysis supported the visual assessment results in terms of the spatial resolution, contrast recovery; uniformity, and the results implied that motion with correction slightly improved image quality compared with the motionless case. The tolerance of the developed method against potential tracking errors had a minimum 10% difference in the amplitude of the rotation angle.

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
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Wienhard K, Schmand M, Casey ME, Baker K, Bao J, Eriksson L, et al. The ECAT HRRT: Performance and first clinical application of the new high resolution research tomograph. IEEE Trans Nucl Sci. 2002;49:104–10.

    Article  Google Scholar 

  2. Yamaya T, Yoshida E, Obi T, Ito H, Yoshikawa K, Murayama H. First human brain imaging by the jPET-D4 prototype with a Pre-computed system Matrix. IEEE Trans Nucl Sci. 2008;55:2482–92.

    Article  Google Scholar 

  3. Yamamoto S, Honda M, Oohashi T, Shimizu K, Senda M. Development of a Brain PET System, PET-Hat: A Wearable PET System for Brain Research. IEEE Transactions on Nuclear Science [Internet]. 2011; 58:668–73. Available from: http://ieeexplore.ieee.org/document/5741875/

  4. Isobe T, Yamada R, Shimizu K, Saito A, Ote K, Sakai K, et al. Development of a new brain PET scanner based on single event data acquisition. 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC) [Internet]. IEEE; 2012; p. 3540–3. Available from: http://ieeexplore.ieee.org/document/6551810/

  5. Tashima H, Ito H, Yamaya T. A proposed helmet-PET with a jaw detector enabling high-sensitivity brain imaging. IEEE Nuclear Science Symposium Conference Record. Institute of Electrical and Electronics Engineers Inc.; 2013.

  6. Tashima H, Yoshida E, Iwao Y, Wakizaka H, Maeda T, Seki C, et al. First prototyping of a dedicated PET system with the hemisphere detector arrangement. Phys Med Biol. 2019; 64:065004. Available from: http://www.ncbi.nlm.nih.gov/pubmed/30673654

  7. Bauer CE, Brefczynski-Lewis J, Marano G, Mandich M-B, Stolin A, Martone P, et al. Concept of an upright wearable positron emission tomography imager in humans. Brain Behav. Wiley-Blackwell; 2016; 6:e00530. Available from: http://www.ncbi.nlm.nih.gov/pubmed/27688946

  8. Johnson CA, Thada S, Rodriguez M, Zhao Y, Iano-Fletcher AR, Liow JS, et al. Software architecture of the MOLAR-HRRT reconstruction engine. IEEE Nuclear Science Symposium Conference Record. Institute of Electrical and Electronics Engineers Inc.; 2004. p. 3956–60.

  9. Costes N, Dagher A, Larcher K, Evans AC, Collins DL, Reilhac A. Motion correction of multi-frame PET data in neuroreceptor mapping: Simulation based validation. NeuroImage Academic Press. 2009;47:1496–505.

    Article  Google Scholar 

  10. Feng T, Yang D, Zhu W, Dong Y, Li H. Real-time data-driven rigid motion detection and correction for brain scan with listmode PET. 2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop, NSS/MIC/RTSD 2016. Institute of Electrical and Electronics Engineers Inc.; 2017.

  11. Thielemans K, Schleyer P, Dunn J, Marsden PK, Manjeshwar RM. Using PCA to detect head motion from PET list mode data. IEEE Nuclear Science Symposium Conference Record. Institute of Electrical and Electronics Engineers Inc.; 2013.

  12. Kyme AZ, Se S, Meikle SR, Fulton RR. Markerless motion estimation for motion-compensated clinical brain imaging. Phys Med Biol. IOP Publishing; 201; 63:105018.

  13. Bloomfield PM, Spinks TJ, Reed J, Schnorr L, Westrip AM, Livieratos L, et al. The design and implementation of a motion correction scheme for neurological PET. Phys Med Biol Institute of Physics Publishing; 2003;48:959–78.

  14. Besl PJ, McKay ND. A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell. 1992;14:239–56.

    Article  Google Scholar 

  15. Olesen OV, Paulsen RR, Højgaard L, Roed B, Larsen R. Motion Tracking in Narrow Spaces: A Structured Light Approach. Springer, Berlin, Heidelberg; 2010. p. 253–60. Available from: http://link.springer.com/https://doi.org/10.1007/978-3-642-15711-0_32

  16. Olesen OV, Paulsen RR, Højgaard L, Roed B, Larsen R. Motion tracking for medical imaging: A nonvisible structured light tracking approach. IEEE Trans Med Imaging. 2012;31:79–87.

    Article  Google Scholar 

  17. Olesen O v., Sullivan JM, Mulnix T, Paulsen RR, Hojgaard L, Roed B, et al. List-mode PET motion correction using markerless head tracking: proof-of-concept with scans of human subject. IEEE Transact Medl Imag. 2013;32:200–9.

  18. Noonan PJ, Howard J, Hallett WA, Gunn RN. Repurposing the Microsoft Kinect for Windows v2 for external head motion tracking for brain PET. Phys Med Biol 2015;60:8753–66. Available from: http://iopscience.iop.org/article/https://doi.org/10.1088/0031-9155/60/22/8753

  19. Kyme AZ, Zhou VW, Meikle SR, Fulton RR. Real-time 3D motion tracking for small animal brain PET. Phys Med Biol 2008; 53:2651–66. Available from: https://pubmed.ncbi.nlm.nih.gov/18443388/

  20. Yoshida E, Tashima H, Akamatsu G, Iwao Y, Takahashi M, Yamashita T, et al. 245 ps-TOF brain-dedicated PET prototype with a hemispherical detector arrangement. Phys Med Biol. Institute of Physics Publishing; 2020;65:145008. Available from: https://doi.org/10.1088/1361-6560/ab8c91

  21. Lorensen WE, Cline HE. Marching cubes: A high resolution 3D surface construction algorithm. ACM SIGGRAPH Computer Graphics. 1987;21(4):163–9.

    Article  Google Scholar 

  22. Iwao Y, Tashima H, Yoshida E, Nishikido F, Ida T, Yamaya T. Seated versus supine: consideration of the optimum measurement posture for brain-dedicated PET. Phys Med Biol. IOP Publishing; 2019; 64:125003. Available from: https://iopscience.iop.org/article/https://doi.org/10.1088/1361-6560/ab221d

  23. Carson RE, Barker WC, Liow JS, Johnson CA. Design of a Motion-compensation OSEM List-mode Algorithm for resolution-recovery Reconstruction for the HRRT. IEEE Nuclear Science Symposium Conference Record. 2003. p. 3281–5.

  24. Jin X, Mulnix T, Gallezot JD, Carson RE. Evaluation of motion correction methods in human brain PET imaging-A simulation study based on human motion data. Med Phys John Wiley and Sons Ltd; 2013;40.

  25. Akamatsu G, Yoshida E, Mikamoto T, Maeda T, Wakizaka H, Tashima H, et al. Development of sealed 22Na phantoms for PET system QA/QC: Uniformity and stability evaluation. 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019. Institute of Electrical and Electronics Engineers Inc.; 2019.

  26. Mann HB, Whitney DR. On a test of whether one of two random variables is stochastically larger than the other. Institute of Mathematical Statistics. 1947;18:50–60. https://doi.org/10.1214/aoms/1177730491.

    Article  Google Scholar 

  27. Kenney JF, Keeping ES. Mathematics of Statistics. 3rd ed. Princeton: Van Nostrand; 1962.

    Google Scholar 

  28. Colsher JG, Muehllehner G. Effects of wobbling motion on image quality in positron tomography. IEEE Trans Nucl Sci. 1981;28:90–3.

    Article  Google Scholar 

Download references

Acknowledgements

This work was partially funded by the Japan Society for the Promotion of Science (JSPS) KAKENHI grant number 17K18376. This work was conducted in collaboration with ATOX Co., Ltd., Japan.

Author information

Authors and Affiliations

  1. Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology, Chiba, Japan

    Yuma Iwao, Go Akamatsu, Hideaki Tashima, Miwako Takahashi & Taiga Yamaya

Authors
  1. Yuma Iwao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Go Akamatsu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hideaki Tashima
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Miwako Takahashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Taiga Yamaya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Yuma Iwao or Taiga Yamaya.

Ethics declarations

Ethical statement

This article does not contain any studies with human or animal subjects performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Iwao, Y., Akamatsu, G., Tashima, H. et al. Marker-less and calibration-less motion correction method for brain PET. Radiol Phys Technol 15, 125–134 (2022). https://doi.org/10.1007/s12194-022-00654-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12194-022-00654-6

Keywords

Search

Navigation