A systematic performance evaluation of head motion correction techniques for 3 commercial PET scanners using a reproducible experimental acquisition protocol

  • Takato Inomata
  • Shoichi Watanuki
  • Hayato Odagiri
  • Takeyuki Nambu
  • Nicolas A. Karakatsanis
  • Hiroshi Ito
  • Hiroshi Watabe
  • Manabu Tashiro
  • Miho ShidaharaEmail author
Original Article



Subject’s motion during brain PET scan degrades spatial resolution and quantification of PET images. To suppress these effects, rigid-body motion correction systems have been installed in commercial PET scanners. In this study, we systematically compare the accuracy of motion correction among 3 commercial PET scanners using a reproducible experimental acquisition protocol.


A cylindrical phantom with two 22Na point sources was placed on a customized base to enable two types of motion, 5° yaw and 15° pitch rotations. Repetitive PET scans (5 min × 5 times) were performed at rest and under 2 motion conditions using 3 clinical PET scanners: the Eminence STARGATE G/L PET/CT (STARGATE) (Shimadzu Corp.), the SET-3000 B/X PET (SET-3000) (Shimadzu Corp.), and the Biograph mMR PET/MR (mMR) (Siemens Healthcare) systems. For STARGATE and SET-3000, the Polaris Vicra (Northern Digital Inc.) optical tracking system was used for frame-by-frame motion correction. For Biograph mMR, sequential MR images were simultaneously acquired with PET and used for LOR-based motion correction. All PET images were reconstructed by FBP algorithm with 1 × 1 mm pixel size. To evaluate the accuracy of motion correction, FWHMs and spherical ROI values were analyzed.


The percent differences (%diff) in averaged FWHMs of point sources at 4 cm off-center between motion-corrected and static images were 0.77 ± 0.16 (STARGATE), 2.4 ± 0.34 (SET-3000), and 11 ± 1.0% (mMR) for a 5° yaw and 2.3 ± 0.37 (STARGATE) and 1.1 ± 0.60 (SET-3000) for a 15° pitch respectively. The averaged %diff between ROI values of motion-corrected images and static images were less than 2.0% for all conditions.


In this study, we proposed a reproducible experimental framework to allow the systematic validation and comparison of multiple motion tracking and correction methodologies among different PET/CT and PET/MR commercial systems. Our proposed validation platform may be useful for future studies evaluating state-of-the-art motion correction strategies in clinical PET imaging.


Brain PET Motion-correction PET/MR Optical tracking Reproducibility 



We would like to thank the research staff at Advanced Clinical Research Center, Fukushima Medical University for helpful support to our experiments. This study was supported in part by Grants-in-Aid for Scientific Research (C) (No. 15K08687) and (B) (No 17H04118) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japanese Government.


This study was supported in part by Grants-in-Aid for Scientific Research (C) (No. 15K08687) and (B) (No 17H04118) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japanese Government.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.


  1. 1.
    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. 2003;48(8):959–78.CrossRefGoogle Scholar
  2. 2.
    Reilhac A, Merida I, Irace Z, Stephenson M, Weekes A, Chen C, et al. Development of a dedicated rebinner with rigid motion correction for the mMR PET/MR scanner, and validation in a large cohort of 11C-PIB scans. J Nucl Med. 2018;59(11):1761–7.CrossRefGoogle Scholar
  3. 3.
    Ren S, Jin X, Chan C, Jian Y, Mulnix T, Liu C, et al. Data-driven event-by-event respiratory motion correction using TOF PET list-mode centroid of distribution. Phys Med Biol. 2017;62(12):4741–55.CrossRefGoogle Scholar
  4. 4.
    Gillman A, Smith J, Thomas P, Rose S, Dowson N. PET motion correction in context of integrated PET/MR: current techniques, limitations, and future projections. Med Phys. 2017;44(12):e430–e445445.CrossRefGoogle Scholar
  5. 5.
    Mukherjee JM, Lindsay C, Mukherjee A, Olivier P, Shao L, King MA, et al. Improved frame-based estimation of head motion in PET brain imaging. Med Phys. 2016;43(5):2443.CrossRefGoogle Scholar
  6. 6.
    Schleyer PJ, Dunn JT, Reeves S, Brownings S, Marsden PK, Thielemans K. Detecting and estimating head motion in brain PET acquisitions using raw time-of-flight PET data. Phys Med Biol. 2015;60(16):6441–588.CrossRefGoogle Scholar
  7. 7.
    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(22):8753–66.CrossRefGoogle Scholar
  8. 8.
    Jiao J, Searle GE, Schnabel JA, Gunn RN. Impact of image-based motion correction on dopamine D3/D2 receptor occupancy-comparison of groupwise and frame-by-frame registration approaches. EJNMMI Phys. 2015;2(1):15.CrossRefGoogle Scholar
  9. 9.
    Huang C, Ackerman JL, Petibon Y, Normandin MD, Brady TJ, El Fakhri G, et al. Motion compensation for brain PET imaging using wireless MR active markers in simultaneous PET-MR: phantom and non-human primate studies. Neuroimage. 2014;91:129–37.CrossRefGoogle Scholar
  10. 10.
    Olesen OV, 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 Trans Med Imaging. 2013;32(2):200–9.CrossRefGoogle Scholar
  11. 11.
    Matsubara K, Ibaraki M, Nakamura K, Yamaguchi H, Umetsu A, Kinoshita F, et al. Impact of subject head motion on quantitative brain (15)O PET and its correction by image-based registration algorithm. Ann Nucl Med. 2013;27(4):335–45.CrossRefGoogle Scholar
  12. 12.
    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. 2013;40(10):102503.CrossRefGoogle Scholar
  13. 13.
    Jin X, Chan C, Mulnix T, Panin V, Casey ME, Liu C, et al. List-mode reconstruction for the Biograph mCT with physics modeling and event-by-event motion correction. Phys Med Biol. 2013;58(16):5567–91.CrossRefGoogle Scholar
  14. 14.
    Ullisch MG, Scheins JJ, Weirich C, Rota Kops E, Celik A, Tellmann L, et al. MR-based PET motion correction procedure for simultaneous MR-PET neuroimaging of human brain. PLoS One. 2012;7(11):e48149.CrossRefGoogle Scholar
  15. 15.
    Olesen OV, Paulsen RR, Hojgaard L, Roed B, Larsen R. Motion tracking for medical imaging: a nonvisible structured light tracking approach. IEEE Trans Med Imaging. 2012;31(1):79–877.CrossRefGoogle Scholar
  16. 16.
    Nazarparvar B, Shamsaei M, Rajabi H. Correction of head movements in positron emission tomography using point source tracking system: a simulation study. Ann Nucl Med. 2012;26(1):7–15.CrossRefGoogle Scholar
  17. 17.
    Ikari Y, Nishio T, Makishi Y, Miya Y, Ito K, Koeppe RA, et al. Head motion evaluation and correction for PET scans with 18F-FDG in the Japanese Alzheimer's disease neuroimaging initiative (J-ADNI) multi-center study. Ann Nucl Med. 2012;26(7):535–44.CrossRefGoogle Scholar
  18. 18.
    Catana C, Benner T, van der Kouwe A, Byars L, Hamm M, Chonde DB, et al. MRI-assisted PET motion correction for neurologic studies in an integrated MR-PET scanner. J Nucl Med. 2011;52(1):154–61.CrossRefGoogle Scholar
  19. 19.
    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. 2009;47(4):1496–505.CrossRefGoogle Scholar
  20. 20.
    Montgomery AJ, Thielemans K, Mehta MA, Turkheimer F, Mustafovic S, Grasby PM. Correction of head movement on PET studies: comparison of methods. J Nucl Med. 2006;47(12):1936–44.Google Scholar
  21. 21.
    Woo SK, Watabe H, Choi Y, Kim KM, Park CC, Bloomfield PM, et al. Sinogram-based motion correction of PET images using optical motion tracking system and list-mode data acquisition. IEEE Trans Nucl Sci. 2004;51(3):782–8.CrossRefGoogle Scholar
  22. 22.
    Fulton R, Meikle S, Eberl S, Pfeiffer J, Constable C, Fulham M. Correction for head movements in positron emission tomography using an optical motion-tracking system. IEEE Trans Nucl Sci. 2002;49:116–23.CrossRefGoogle Scholar
  23. 23.
    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. In: 2016 IEEE nuclear science symposium, medical imaging conference and room-temperature semiconductor detector workshop (NSS/MIC/RTSD), Strasbourg, 2016. pp. 1–4Google Scholar
  24. 24.
    Karakatsanis NA, Robson PM, Dweck MR, Trivieri MG, Calcagno C, Mani V, et al. PET-driven respiratory phase tracking and self-gating of PET data: clinical demonstration of enhanced lesion detectability in cardiovascular PET/MRI. 2017 ieee nuclear science symposium and medical imaging conference (NSS/MIC); Atlanta, 2017. pp. 1–6Google Scholar
  25. 25.
    Picard Y, Thompson CJ. Motion correction of PET images using multiple acquisition frames. IEEE Trans Med Imaging. 1997;16(2):137–44.CrossRefGoogle Scholar
  26. 26.
    Mohy-ud-Din H, Karakatsanis NA, Goddard JS, Baba J, Wills W, Tahari AK, et al. Generalized dynamic PET inter-frame and intra-frame motion correction-Phantom and human validation studies. IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC); Anaheim, 2012. pp. 3067–78.Google Scholar
  27. 27.
    Raghunath N, Faber TL, Suryanarayanan S, Votaw JR. Motion correction of PET brain images through deconvolution: II. Practical implementation and algorithm optimization. Phys Med Biol. 2009;54(3):813–29.Google Scholar
  28. 28.
    Fulton R, Tellmann L, Pietrzyk U, Winz O, Stangier I, Nickel I, et al. Accuracy of motion correction methods for pet brain imaging. IEEE Trans Med Imaging. 2004;7:4226–300.Google Scholar
  29. 29.
    Jin X, Mulnix T, Sandiego CM, Carson RE. Evaluation of frame-based and event-by-event motion-correction methods for awake monkey brain PET imaging. J Nucl Med. 2014;55(2):287–93.CrossRefGoogle Scholar
  30. 30.
    Koshino K, Watabe H, Enmi J, Hirano Y, Zeniya T, Hasegawa S, et al. Effects of patient movement on measurements of myocardial blood flow and viability in resting (1)(5)O-water PET studies. J Nucl Cardiol. 2012;19(3):524–33.CrossRefGoogle Scholar
  31. 31.
    Matsumoto K, Kitamura K, Mizuta T, Shimizu K, Murase K, Senda M. Accuracy of attenuation coefficient obtained by 137Cs single-transmission scanning in PET: comparison with conventional germanium line source. Nihon Hosyasen GIjitsu Kakkai Zasshi. 2006;62(2):289–96.CrossRefGoogle Scholar
  32. 32.
    Martinez-Moller A, Souvatzoglou M, Delso G, Bundschuh RA, Chefd'hotel C, Ziegler SI, et al. Tissue classification as a potential approach for attenuation correction in whole-body PET/MRI: evaluation with PET/CT data. J Nucl Med. 2009;50(4):520–6.CrossRefGoogle Scholar
  33. 33.
    Delso G, Furst S, Jakoby B, Ladebeck R, Ganter C, Nekolla SG, et al. Performance measurements of the Siemens mMR integrated whole-body PET/MR scanner. J Nucl Med. 2011;52(12):1914–22.CrossRefGoogle Scholar
  34. 34.
    Shidahara M, Thomas BA, Okamura N, Ibaraki M, Matsubara K, Oyama S, et al. A comparison of five partial volume correction methods for Tau and Amyloid PET imaging with [(18)F]THK5351 and [(11)C]PIB. Ann Nucl Med. 2017;31(7):563–9.CrossRefGoogle Scholar
  35. 35.
    Zhang X, Zhou J, Cherry SR, Badawi RD, Qi J. Quantitative image reconstruction for total-body PET imaging using the 2-meter long EXPLORER scanner. Phys Med Biol. 2017;62(6):2465–85.CrossRefGoogle Scholar
  36. 36.
    Cherry SR, Badawi RD, Karp JS, Moses WW, Price P, Jones T. Total-body imaging: transforming the role of positron emission tomography. Sci Transl Med. 2017;9(381):eaaf6169.CrossRefGoogle Scholar

Copyright information

© The Japanese Society of Nuclear Medicine 2019

Authors and Affiliations

  1. 1.Division of Medical PhysicsTohoku University Graduate School of MedicineSendaiJapan
  2. 2.Division of Cyclotron Nuclear Medicine, Cyclotron and Radioisotope CenterTohoku UniversitySendaiJapan
  3. 3.Department of Diagnostic RadiologyTohoku University HospitalSendaiJapan
  4. 4.Advanced Clinical Research CenterFukushima Medical UniversityFukushimaJapan
  5. 5.Division of Radiopharmaceutical Sciences, Department of Radiology, Weill Cornell Medical CollegeCornell UniversityIthacaUSA
  6. 6.Department of Radiology and Nuclear MedicineFukushima Medical UniversityFukushimaJapan
  7. 7.Division of Radiation Protection and Safety Control, Cyclotron and Radioisotope CenterTohoku UniversitySendaiJapan
  8. 8.Division of Applied Quantum Medical Engineering, Department of Quantum Science and Energy Engineering, Graduate School of EngineeringTohoku UniversitySendaiJapan

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