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Performance evaluation of dedicated brain PET scanner with motion correction system

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Annals of Nuclear Medicine Aims and scope Submit manuscript

Abstract

Objective

Various motion correction (MC) algorithms for positron emission tomography (PET) have been proposed to accelerate the diagnostic performance and research in brain activity and neurology. We have incorporated MC system-based optical motion tracking into the brain-dedicated time-of-flight PET scanner. In this study, we evaluate the performance characteristics of the developed PET scanner when performing MC in accordance with the standards and guidelines for the brain PET scanner.

Methods

We evaluate the spatial resolution, scatter fraction, count rate characteristics, sensitivity, and image quality of PET images. The MC evaluation is measured in terms of the spatial resolution and image quality that affect movement.

Results

In the basic performance evaluation, the average spatial resolution by iterative reconstruction was 2.2 mm at 10 mm offset position. The measured peak noise equivalent count rate was 38.0 kcps at 16.7 kBq/mL. The scatter fraction and system sensitivity were 43.9% and 22.4 cps/(Bq/mL), respectively. The image contrast recovery was between 43.2% (10 mm sphere) and 72.0% (37 mm sphere). In the MC performance evaluation, the average spatial resolution was 2.7 mm at 10 mm offset position, when the phantom stage with the point source translates to ± 15 mm along the y-axis. The image contrast recovery was between 34.2 % (10 mm sphere) and 66.8 % (37 mm sphere).

Conclusions

The reconstructed images using MC were restored to their nearly identical state as those at rest. Therefore, it is concluded that this scanner can observe more natural brain activity.

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Fig. 1
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source placements for the MC evaluation in spatial resolution measurement (A axial view and B coronal view). The point source was placed on a hollow phantom with retro-reflective markers to assume the measurement of the human head

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References

  1. Meikle SR, Sossi V, Roncali E, Cherry SR, Banati R, Mankoff D, et al. Quantitative PET in the 2020s: a roadmap. Phys Med Biol. 2021;66:06RM01.

    Article  CAS  PubMed  Google Scholar 

  2. Watanabe M, Shimizu K, Omura T, Takahashi M, Kosugi T, Yoshikawa E, et al. A new high-resolution PET scanner dedicated to brain research. IEEE Trans Nucl Sci. 2002;49:634–9.

    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 Trans Nucl Sci. 2011;58:668–73.

    Article  Google Scholar 

  4. Jung J, Choi Y, Jung JH, Kim S, Im KC. Performance evaluation of neuro-PET using silicon photomultipliers. Nucl Instrum Methods A. 2016;819:182–7.

    Article  CAS  Google Scholar 

  5. 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.

    Article  CAS  PubMed  Google Scholar 

  6. Moliner L, Rodríguez-Alvarez MJ, Catret JV, González A, Ilisie V, Benlloch JM. NEMA performance evaluation of CareMiBrain dedicated brain PET and comparison with the whole-body and dedicated brain PET systems. Sci Rep. 2019;9:15484.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. 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 

  8. Yoshida E, Kobayashi A, Yamaya T, Watanabe M, Nishikido F, Kitamura K, et al. The jPET-D4: Performance evaluation of four-layer DOI-PET scanner using the NEMA NU2-2001 standard. In: 2006 IEEE nuclear science symposium and medical imaging conference. 2006:2532–6.

  9. Gong K, Majewski S, Kinahan PE, Harrison RL, Elston BF, Manjeshwar R, et al. Designing a compact high performance brain PET scanner–simulation study. Phys Med Biol. 2016;61:3681–97.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Watanabe M, Saito A, Isobe T, Ote K, Yamada R, Moriya T, et al. Performance evaluation of a high- resolution brain PET scanner using four-layer MPPC DOI detectors. Phys Med Biol. 2017;62:7148–66.

    Article  CAS  PubMed  Google Scholar 

  11. Akamatsu G, Tashima H, Iwao Y, Wakizaka H, Maeda T, Mohammadi A, et al. T. performance evaluation of a whole-body prototype PET scanner with four-layer DOI detectors. Phys Med Biol. 2019;64:095014.

    Article  CAS  PubMed  Google Scholar 

  12. Sluis J, Jong J, Schaar J, Noordzij W, Snick P, Dierckx R, et al. Performance characteristics of the digital biograph vision PET/CT System. J Nucl Med. 2019;60:1031–6.

    Article  PubMed  CAS  Google Scholar 

  13. 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. 2020;65: 145008.

    Article  CAS  PubMed  Google Scholar 

  14. Vandenberghe S, Mikhaylova E, D’Hoe E, Mollet P, Karp JS. Recent developments in time-of-flight PET. EJNMMI Phys. 2016;3:3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Surti S, Karp JS. Update on latest advances in time-of-flight PET. Phys Med. 2020;80:251–8.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Kwon SI, Ota R, Berg E, Hashimoto F, Nakajima K, Ogawa I, et al. Ultrafast timing enables reconstruction-free positron emission imaging. Nat Photon. 2021;15:914–8.

    Article  CAS  Google Scholar 

  17. Rahmin A, Rousset O, Zaidi H. Strategies for motion tracking and correction in PET. PET Clin. 2007;2:251–66.

    Article  Google Scholar 

  18. Herzog H, Tellmann L, Fulton R, Stangier I, Kops ER, Bente K, et al. Motion artifact reduction on parametric PET images of neuroreceptor binding. J Nucl Med. 2005;46:1059–65.

    PubMed  Google Scholar 

  19. Zaidi H, Montandon M, Meikle S. Strategies for attenuation compensation in neurological PET studies. Neuroimage. 2006;34:518–41.

    Article  PubMed  Google Scholar 

  20. Inubushi T, Ito M, Mori Y, Futatsubashi M, Sato K, Ito S, et al. Neural correlates of head restraint: unsolicited neuronal activation and dopamine release. Neuroimage. 2021;224: 117434.

    Article  CAS  PubMed  Google Scholar 

  21. Kyme AZ, Fulton RR. Motion estimation and correction in SPECT. PET and CT Phys Med Biol. 2021;66:18TR02.

    Article  Google Scholar 

  22. Keller SH, Sibomana M, Olesen OV, Svarer C, Holm S, Andersen FL, et al. Methods for motion correction evaluation using 18F-FDG human brain scans on a high-resolution PET scanner. J Nucl Med. 2012;53:495–504.

    Article  CAS  PubMed  Google Scholar 

  23. 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.

    Article  CAS  PubMed  Google Scholar 

  24. Iwao Y, Akamatsu G, Tashima H, Takahashi M, Yamaya T. Marker-less and calibration-less motion correction method for brain PET. Radiol Phys Technol. 2022;5:125–34.

  25. Picard Y, Thompson CJ. Motion correction of PET images using multiple acquisition frames. IEEE Trans Med Imag. 1997;16:137–44.

    Article  CAS  Google Scholar 

  26. Slipsager JM, Ellegaard AH, Glimberg SL, Paulsen RR, Tisdall MD, Wighton P, et al. Markerless motion tracking and correction for PET, MRI, and simultaneous PET/MRI. PLOS One. 2019;14: 0215524.

    Article  CAS  Google Scholar 

  27. Rakvongthai Y, Fakhri GE. Magnetic resonance-based motion correction for quantitative PET in simultaneous PET-MR Imaging. PET Clin. 2017;12:321–7.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Saito A, Yoshikawa E, Omura T, Yamanaka T, Ote K, Isobe T, et al. Development of a brain PET scanner with motion correction using motion capture technology. IEEE Nuclear Science Symposium and Medical Imaging Conference. 2018:M-07-146.

  29. Akamatsu G, Tashima H, Yoshida E, Wakizaka H, Iwao Y, Maeda T, et al. Modified NEMA NU-2 performance evaluation methods for a brain-dedicated PET system with a hemispherical detector arrangement. Biomed Phys Eng Express. 2019;6: 015012.

    Article  PubMed  Google Scholar 

  30. Industries Association of Radiation Apparatus (JESRA): Performance evaluation of positron emission tomographs X-0073*G-2019: 2019

  31. Ota R. Photon counting detectors and their applications ranging from particle physics experiments to environmental radiation monitoring and medical imaging. Radiol Phys Technol. 2021;14:134–48.

    Article  PubMed  Google Scholar 

  32. Yamaya T, Inaniwa T, Minohara S, Yoshida E, Inadama N, Nishikido F, et al. A proposal of an open PET geometry. Phys Med Biol. 2008;53:757–73.

    Article  PubMed  Google Scholar 

  33. Zhang Z. A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell. 2000;12:1330–4.

    Article  Google Scholar 

  34. Tanaka E, Kudo H. Optimal relaxation parameters of DRAMA (Dynamic RAMLA) aiming at one-pass image reconstruction for 3D-PET. Phys Med Biol. 2010;55:2917–39.

    Article  PubMed  Google Scholar 

  35. Badawi RD, Marsden PK. Developments in component-based normalization for 3D PET. Phys Med Biol. 1999;44:571–94.

    Article  CAS  PubMed  Google Scholar 

  36. Zaidi H, Hasegawa B. Determination of the attenuation map in emission tomography. J Nucl Med. 2003;44:291–315.

    PubMed  Google Scholar 

  37. Watson C. New, faster, image-based scatter correction for 3D PET. IEEE Trans Nucl Sci. 2000;47:1587–94.

    Article  Google Scholar 

  38. Rahmim A, Lenox M, Reader AJ, Michel C, Burbar Z, Ruth TJ, et al. Statistical list-mode image reconstruction for the high resolution research tomograph. Phys Med Biol. 2004;49:4239–58.

    Article  CAS  PubMed  Google Scholar 

  39. Jin X, Mulnix T, Gallezot J, Carson RE. Evaluation of motion correction methods in human brain PET imaging–A simulation study based on human motion data. Med Phys. 2013;40: 102503.

    Article  PubMed  PubMed Central  Google Scholar 

  40. 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. 2019;64: 125003.

    Article  PubMed  Google Scholar 

  41. Zein SA, Karakatsanis NA, Conti M, Nehmeh SA. Monte Carlo simulation of the siemens biograph vision PET with extended axial field of view using sparse detector module rings configuration. IEEE Trans Radiat Plasma Med Sci. 2021;5:331–42.

    Article  Google Scholar 

  42. Lehnert W, Riss PJ, Mendoza A, Lopez S, Fernandez G, Ilheu M, et al. Whole-body biodistribution and radiation dosimetry of [18 F] PR04.MZ: a new PET radiotracer for clinical management of patients with movement disorders. EJNMMI Res. 2022;12:1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Hoffman M, Bezrukov I, Mantlik F, Aschoff P, Steinke F, Bayer T, et al. MRI-based attenuation correction for whole-body PET/MRI: quantitative evaluation of segmentation- and atlas-based methods. J Nucl Med. 2011;9:1392–9.

    Article  Google Scholar 

  44. Rezaei A, Defrise M, Bal G, Michel C, Conti M, Watson C, et al. Simultaneous reconstruction of activity and attenuation in time-of-flight PET. IEEE Trans Med Imag. 2012;12:2224–33.

    Article  Google Scholar 

  45. Hashimoto F, Ito M, Ote K, Isobe T, Okada H, Ouchi Y. Deep learning-based attenuation correction for brain PET with various radiotracers. Ann Nucl Med. 2021;6:691–701.

    Article  CAS  Google Scholar 

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Acknowledgements

The authors would like to thank the following people for their scientific advice and technical assistance, in alphabetical order: Akihiro Kakimoto, Akinori Saito, Kibo Ote, Mineo Egawa, Mitsuo Watanabe, Ryosuke Ota, and Takahiro Moriya. We are grateful to Aki Sato, Hatsumi Takahashi, Hiroyuki Okada, Kyoji Matsuyama, Nobutoshi Nakamura, Toru Hirohata, and Yutaka Yamashita for administrative assistance. The authors gratefully acknowledge the staff of Hamamatsu Medical Imaging Center, Hamamatsu Medical Photonics Foundation, for providing technical assistance.

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Author notes

  1. Yuya Onishi, Takashi Isobe, and Masanori Ito have contributed equally to this work.

Authors and Affiliations

  1. Central Research Laboratory, Hamamatsu Photonics K. K, Hamamatsu, 434-8601, Japan

    Yuya Onishi, Takashi Isobe, Fumio Hashimoto, Tomohide Omura & Etsuji Yoshikawa

  2. Global Strategic Challenge Center, Hamamatsu Photonics K.K, Hamamatsu, 434-8601, Japan

    Masanori Ito

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  1. Yuya Onishi
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Correspondence to Yuya Onishi.

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Y.O., T.I., M.I., F.H., T.O., and E.Y. are employees of Hamamatsu Photonics K.K. The company had no control over the interpretation, writing, or publication of this work.

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Onishi, Y., Isobe, T., Ito, M. et al. Performance evaluation of dedicated brain PET scanner with motion correction system. Ann Nucl Med 36, 746–755 (2022). https://doi.org/10.1007/s12149-022-01757-1

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