Skip to main content
SpringerLink
Log in

PET/MRI: Motion Correction

  • Chapter
  • First Online:
PET/MRI in Oncology

Abstract

Simultaneous PET/MR imaging allows the use of motion estimates derived from one modality to perform motion compensation of the data acquired with the other technique. Some of the most recently proposed MR-based PET motion estimation and correction techniques will be discussed in this chapter. First, the MR-based techniques for head, respiratory, cardiac, and bulk motion characterization will be introduced. Next, the algorithms for performing the actual motion correction using these estimates will be briefly covered. Finally, the various methods proposed for the qualitative and quantitative assessment of the impact of motion correction will be reviewed.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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. Drzezga A, Souvatzoglou M, Eiber M, et al. First clinical experience with integrated whole-body PET/MR: comparison to PET/CT in patients with oncologic diagnoses. J Nucl Med. 2012;53:845–55.

    PubMed  Google Scholar 

  2. Levin C, Glover G, Deller T, et al. Prototype time-of-flight PET ring integrated with a 3 T MRI system for simultaneous whole-body PET/MR imaging. J Nucl Med. 2013;54:148.

    Google Scholar 

  3. Kesner AL, Schleyer PJ, Büther F, et al. On transcending the impasse of respiratory motion correction applications in routine clinical imaging – a consideration of a fully automated data driven motion control framework. EJNMMI Phys. 2014;1:8.

    PubMed  PubMed Central  Google Scholar 

  4. Catana C. Motion correction options in PET/MRI. Semin Nucl Med. 2015;45:212–23.

    PubMed  PubMed Central  Google Scholar 

  5. Allen AM, Siracuse KM, Hayman JA, et al. Evaluation of the influence of breathing on the movement and modeling of lung tumors. Int J Radiat Oncol Biol Phys. 2004;58:1251–7.

    PubMed  Google Scholar 

  6. McLeish K, Hill DL, Atkinson D, et al. A study of the motion and deformation of the heart due to respiration. IEEE Trans Med Imaging. 2002;21:1142–50.

    PubMed  Google Scholar 

  7. Suramo I, Paivansalo M, Myllyla V. Cranio-caudal movements of the liver, pancreas and kidneys in respiration. Acta Radiol Diagn. 1984;25:129–31.

    CAS  Google Scholar 

  8. Korin HW, Ehman RL, Riederer SJ, et al. Respiratory kinematics of the upper abdominal organs - a quantitative study. Magn Reson Med. 1992;23:172–8.

    CAS  PubMed  Google Scholar 

  9. Clifford MA, Banovac F, Levy E, et al. Assessment of hepatic motion secondary to respiration for computer assisted interventions. Comput Aided Surg. 2002;7:291–9.

    PubMed  Google Scholar 

  10. Shimizu S, Shirato H, Aoyama H, et al. High-speed magnetic resonance imaging for four-dimensional treatment planning of conformal radiotherapy of moving body tumors. Int J Radiat Oncol Biol Phys. 2000;48:471–4.

    CAS  PubMed  Google Scholar 

  11. Brandner ED, Wu A, Chen H, et al. Abdominal organ motion measured using 4D CT. Int J Radiat Oncol Biol Phys. 2006;65:554–60.

    PubMed  Google Scholar 

  12. Feng M, Balter JM, Normolle D, et al. Characterization of pancreatic tumor motion using cine MRI: surrogates for tumor position should be used with caution. Int J Radiat Oncol Biol Phys. 2009;74:884–91.

    PubMed  PubMed Central  Google Scholar 

  13. Mori S, Hara R, Yanagi T, et al. Four-dimensional measurement of intrafractional respiratory motion of pancreatic tumors using a 256 multi-slice CT scanner. Radiother Oncol. 2009;92:231–7.

    PubMed  Google Scholar 

  14. Yamashita H, Yamashita M, Futaguchi M, et al. Individually wide range of renal motion evaluated by four-dimensional computed tomography. SpringerPlus. 2014;3:131.

    PubMed  PubMed Central  Google Scholar 

  15. Dinkel J, Thieke C, Plathow C, et al. Respiratory-induced prostate motion: characterization and quantification in dynamic MRI. Strahlenther Onkol. 2011;187:426–32.

    PubMed  Google Scholar 

  16. Sengupta PP, Korinek J, Belohlavek M, et al. Left ventricular structure and function: basic science for cardiac imaging. J Am Coll Cardiol. 2006;48:1988–2001.

    PubMed  Google Scholar 

  17. Catana C, Benner T, van der Kouwe A, et al. MRI-assisted PET motion correction for neurologic studies in an integrated MR-PET scanner. J Nucl Med. 2011;52:154–61.

    PubMed  Google Scholar 

  18. Thesen S, Heid O, Mueller E, et al. Prospective acquisition correction for head motion with image-based tracking for real-time fMRI. Magn Reson Med. 2000;44:457–63.

    CAS  PubMed  Google Scholar 

  19. van der Kouwe AJW, Benner T, Dale AM. Real-time rigid body motion correction and shimming using cloverleaf navigators. Magn Reson Med. 2006;56:1019–32.

    PubMed  Google Scholar 

  20. Scheins J, Ullisch M, Tellmann L, et al. MR-guided PET motion correction in LOR space using generic projection data for image reconstruction with PRESTO. Nucl Instrum Methods Phys Res Sect A. 2013;702:64–6.

    CAS  Google Scholar 

  21. Ullisch MG, Scheins J, Weirich C, et al. MR-guided data framing for PET motion correction in simultaneous MR-PET: a preliminary evaluation. Nucl Instrum Methods Phys Res Sect A. 2013;702:67–9.

    CAS  Google Scholar 

  22. Ullisch MG, Scheins JJ, Weirich C, et al. MR-based PET motion correction procedure for simultaneous MR-PET neuroimaging of human brain. PLoS One. 2012;7:e48149.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Tisdall MD, Hess AT, Reuter M, et al. Volumetric navigators for prospective motion correction and selective reacquisition in neuroanatomical MRI. Magn Reson Med. 2012;68:389–99.

    PubMed  Google Scholar 

  24. Ackerman JL, Offutt MC, Buxton RB, et al. Rapid 3D tracking of small RF coils. Paper presented at: Proceedings of the 5th annual meeting of SMRM, Montreal, Canada. 1986.

    Google Scholar 

  25. Huang C, Ackerman JL, Petibon Y, 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.

    PubMed  Google Scholar 

  26. McClelland JR, Hawkes DJ, Schaeffter T, et al. Respiratory motion models: a review. Med Image Anal. 2013;17:19–42.

    CAS  PubMed  Google Scholar 

  27. Ehman RL, Felmlee JP. Adaptive technique for high-definition MR imaging of moving structures. Radiology. 1989;173:255–63.

    CAS  PubMed  Google Scholar 

  28. Grimm R, Furst S, Dregely I, et al. Self-gated radial MRI for respiratory motion compensation on hybrid PET/MR systems. Med Image Comput Comput Assist Interv. 2013;16:17–24.

    PubMed  Google Scholar 

  29. Furst S, Grimm R, Hong I, et al. Motion correction strategies for integrated PET/MR. J Nucl Med. 2015;56:261–9.

    PubMed  Google Scholar 

  30. Wurslin C, Schmidt H, Martirosian P, et al. Respiratory motion correction in oncologic PET using T1-weighted MR imaging on a simultaneous whole-body PET/MR system. J Nucl Med. 2013;54:464–71.

    PubMed  Google Scholar 

  31. Fayad H, Schmidt H, Wuerslin C, et al. Reconstruction-incorporated respiratory motion correction in clinical simultaneous PET/MR imaging for oncology applications. J Nucl Med. 2015;56:884–9.

    PubMed  Google Scholar 

  32. Manber R, Thielemans K, Hutton BF, et al. Joint PET-MR respiratory motion models for clinical PET motion correction. Phys Med Biol. 2016;61:6515–30.

    PubMed  Google Scholar 

  33. Dutta J, Huang C, Li Q, et al. Pulmonary imaging using respiratory motion compensated simultaneous PET/MR. Med Phys. 2015;42:4227–40.

    PubMed  PubMed Central  Google Scholar 

  34. Heinrich MP, Jenkinson M, Brady SM, et al. Globally optimal deformable registration on a minimum spanning tree using dense displacement sampling. In: Ayache N, Delingette H, Golland P, Mori K, editors. Medical image computing and computer-assisted intervention – MICCAI 2012: 15th international conference, Nice, France, October 1–5, 2012, proceedings, part III. Berlin, Heidelberg: Springer; 2012. p. 115–22.

    Google Scholar 

  35. Kroon DJ, Slump CH. MRI modality transformation in demon registration. Paper presented at: 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro; June 28–July 1 2009. 2009.

    Google Scholar 

  36. Sorzano COS, Thévenaz P, Unser M. Elastic registration of biological images using vector-spline regularization. IEEE Trans Biomed Eng. 2005;52:652–63.

    PubMed  Google Scholar 

  37. Ozturk C, Derbyshire JA, McVeigh ER. Estimating motion from MRI data. Proc IEEE. 2003;91:1627–48.

    Google Scholar 

  38. Guerin B, Cho S, Chun SY, et al. Nonrigid PET motion compensation in the lower abdomen using simultaneous tagged-MRI and PET imaging. Med Phys. 2011;38:3025–38.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Chun SY, Reese TG, Ouyang JS, et al. MRI-Based nonrigid motion correction in simultaneous PET/MRI. J Nucl Med. 2012;53:1284–91.

    PubMed  Google Scholar 

  40. Scott AD, Keegan J, Firmin DN. Motion in cardiovascular MR imaging. Radiology. 2009;250:331–51.

    PubMed  Google Scholar 

  41. Lenz GW, Haacke EM, White RD. Retrospective cardiac gating: a review of technical aspects and future directions. Magn Reson Imaging. 1989;7:445–55.

    CAS  PubMed  Google Scholar 

  42. Larson AC, White RD, Laub G, et al. Self-gated cardiac cine MRI. Magn Reson Med. 2004;51:93–102.

    PubMed  PubMed Central  Google Scholar 

  43. Crowe ME, Larson AC, Zhang Q, et al. Automated rectilinear self-gated cardiac cine imaging. Magn Reson Med. 2004;52:782–8.

    PubMed  Google Scholar 

  44. Zerhouni EA, Parish DM, Rogers WJ, et al. Human-heart-tagging with MR imaging - a method for noninvasive assessment of myocardial motion. Radiology. 1988;169:59–63.

    CAS  PubMed  Google Scholar 

  45. Axel L, Dougherty L. MR imaging of motion with spatial modulation of magnetization. Radiology. 1989;171:841–5.

    CAS  PubMed  Google Scholar 

  46. Mosher TJ, Smith MB. A DANTE tagging sequence for the evaluation of translational sample motion. Magn Reson Med. 1990;15:334–9.

    CAS  PubMed  Google Scholar 

  47. Petibon Y, Ouyang J, Zhu X, et al. Cardiac motion compensation and resolution modeling in simultaneous PET-MR: a cardiac lesion detection study. Phys Med Biol. 2013;58:2085.

    CAS  PubMed  Google Scholar 

  48. Ledesma-Carbayo MJ, Derbyshire JA, Sampath S, et al. Unsupervised estimation of myocardial displacement from tagged MR sequences using nonrigid registration. Magn Reson Med. 2008;59:181–9.

    PubMed  Google Scholar 

  49. Huang C, Petibon Y, Ouyang J, et al. Accelerated acquisition of tagged MRI for cardiac motion correction in simultaneous PET-MR: phantom and patient studies. Med Phys. 2015;42:1087–97.

    PubMed  PubMed Central  Google Scholar 

  50. Petibon Y, El Fakhri G, Nezafat R, et al. Towards coronary plaque imaging using simultaneous PET-MR: a simulation study. Phys Med Biol. 2014;59:1203–22.

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Munoz C, Neji R, Cruz G, et al. Motion-corrected simultaneous cardiac positron emission tomography and coronary MR angiography with high acquisition efficiency. Magn Reson Med. 2017. doi:10.1002/mrm.26690.

    Google Scholar 

  52. Pruessmann KP, Weiger M, Scheidegger MB, et al. SENSE: Sensitivity encoding for fast MRI. Magn Reson Med. 1999;42:952–62.

    CAS  PubMed  Google Scholar 

  53. Wang X, Rahmim A, Tang J. MRI assisted dual motion correction for myocardial perfusion defect detection in PET imaging. Med Phys. 2017;44(9):4536–47. doi: 10.1002/mp.12429.

    Google Scholar 

  54. Kolbitsch C, Ahlman MA, Davies-Venn C, et al. Cardiac and respiratory motion correction for simultaneous cardiac PET/MR. J Nucl Med. 2017;58:846–52.

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Kolbitsch C, Prieto C, Tsoumpas C, et al. A 3D MR-acquisition scheme for nonrigid bulk motion correction in simultaneous PET-MR. Med Phys. 2014;41:082304.

    PubMed  Google Scholar 

  56. Livieratos L, Stegger L, Bloomfield PM, et al. Rigid-body transformation of list-mode projection data for respiratory motion correction in cardiac PET. Phys Med Biol. 2005;50:3313–22.

    CAS  PubMed  Google Scholar 

  57. Lamare F, Ledesma Carbayo MJ, Cresson T, et al. List-mode-based reconstruction for respiratory motion correction in PET using non-rigid body transformations. Phys Med Biol. 2007;52:5187–204.

    CAS  PubMed  Google Scholar 

  58. Rank CM, Heusser T, Wetscherek A, et al. Respiratory motion compensation for simultaneous PET/MR based on highly undersampled MR data. Med Phys. 2016;43:6234.

    PubMed  Google Scholar 

  59. Manber R, Thielemans K, Hutton BF, et al. Practical PET respiratory motion correction in clinical PET/MR. J Nucl Med. 2015;56:890–6.

    PubMed  Google Scholar 

  60. Pedemonte S, Catana C, Van Leemput K. 4-D PET-MR with volumetric navigators and compressed sensing. In: Gao F, Shi K, Li S, editors. Computational methods for molecular imaging. Cham: Springer; 2015. p. 93–101.

    Google Scholar 

  61. Boyd S, Parikh N, Chu E, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends® Mach Learn. 2011;3:1–122.

    Google Scholar 

  62. Petibon Y, Guehl NJ, Reese TG, et al. Impact of motion and partial volume effects correction on PET myocardial perfusion imaging using simultaneous PET-MR. Phys Med Biol. 2017;62:326–43.

    PubMed  Google Scholar 

  63. Chonde DB, Izquierdo-Garcia D, Chen K, et al. Masamune: a tool for automatic dynamic PET data processing, image reconstruction and integrated PET/MRI data analysis. EJNMMI Phys. 2014;1:A57.

    PubMed  PubMed Central  Google Scholar 

  64. Roffman JL, Tanner AS, Eryilmaz H, et al. Dopamine D-1 signaling organizes network dynamics underlying working memory. Sci Adv. 2016;2:10.

    Google Scholar 

  65. Atzil S, Touroutoglou A, Rudy T, et al. Dopamine in the medial amygdala network mediates human bonding. Proc Natl Acad Sci U S A. 2017;114:2361–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Chen KT, Hutchcroft W, Salcedo S, et al. Improved quantification of dementia PET data using temporally and spatially correlated MR data. In: World molecular imaging conference. New York, NY. 2016.

    Google Scholar 

  67. Townsend DW, Carney JP, Yap JT, et al. PET/CT today and tomorrow. J Nucl Med. 2004;45(Suppl 1):4S–14S.

    PubMed  Google Scholar 

  68. Kinahan PE, Townsend DW, Beyer T, et al. Attenuation correction for a combined 3D PET/CT scanner. Med Phys. 1998;25:2046–53.

    CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA

    Ciprian Catana

Authors
  1. Ciprian Catana
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ciprian Catana .

Editor information

Editors and Affiliations

  1. Division of Nuclear Medicine and Molecular Imaging, Stanford University, Stanford, CA, USA

    Andrei Iagaru

  2. Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA

    Thomas Hope

  3. Dept. of Nuclear Medicine, University of Zurich, Zurich, Switzerland

    Patrick Veit-Haibach

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Catana, C. (2018). PET/MRI: Motion Correction. In: Iagaru, A., Hope, T., Veit-Haibach, P. (eds) PET/MRI in Oncology. Springer, Cham. https://doi.org/10.1007/978-3-319-68517-5_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-68517-5_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68516-8

  • Online ISBN: 978-3-319-68517-5

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation