In vivo validation of spatio-temporal liver motion prediction from motion tracked on MR thermometry images

  • C. Tanner
  • Y. Zur
  • K. French
  • G. Samei
  • J. Strehlow
  • G. Sat
  • H. McLeod
  • G. Houston
  • S. Kozerke
  • G. Székely
  • A. Melzer
  • T. Preusser
Original Article

Abstract

Purpose

Magnetic resonance-guided focused ultrasound (MRgFUS) of the liver during free-breathing requires spatio-temporal prediction of the liver motion from partial motion observations. The study purpose is to evaluate the prediction accuracy for a realistic MRgFUS therapy scenario, namely for human in vivo data, tracking based on MR images routinely acquired during MRgFUS and in vivo deformations caused by the FUS probe.

Methods

In vivo validation of the motion model was based on a 3D breath-hold image and an interleaved acquisition of two MR slices. Prediction accuracy was determined with respect to manually annotated landmarks. A statistical population liver motion model was used for predicting the liver motion for not tracked regions. This model was individualized by mapping it to end-exhale 3D breath-hold images. Spatial correspondence between tracking and model positions was established by affine 3D-to-2D image registration. For spatio-temporal prediction, MR tracking results were temporally extrapolated.

Results

Performance was evaluated for 10 volunteers, of which 5 had a dummy FUS probe put on their abdomen. MR tracking had a mean (95 %) accuracy of 1.1 (2.4) mm. The motion of the liver on the evaluation MR slice was spatio-temporally predicted with an accuracy of 1.9 (4.4) mm for a latency of 216 ms. A simple translation model performed similarly (2.1 (4.8) mm) as the two MR slices were relatively close (mean 38 mm). Temporal prediction was important (10 % error reduction), while registration effects could only partially be assessed and showed no benefits. On average, motion magnitude, motion amplitude and breathing frequency increased by 24, 16 and 8 %, respectively, for the cases with FUS probe placement. This motion increase could be reduced by the spatio-temporal prediction.

Conclusion

The study shows that tracking liver vessels on MR images, which are also used for MR thermometry, is a viable approach.

Keywords

Focused ultrasound Respiration Motion prediction  Tracking 

References

  1. 1.
    Ahrendt P (2005) The multivariate Gaussian probability distribution. Tech. repGoogle Scholar
  2. 2.
    Arnold P, Preiswerk F, Fasel B, Salomir R, Scheffler K, Cattin P (2011) 3D organ motion prediction for MR-guided high intensity focused ultrasound. In: Medical image computing and computer-assisted intervention, pp 623–630Google Scholar
  3. 3.
    Blackall J, Ahmad S, Miquel M, McClelland J, Landau D, Hawkes D (2006) MRI-based measurements of respiratory motion variability and assessment of imaging strategies for radiotherapy planning. Phys Med Biol 51:4147CrossRefPubMedGoogle Scholar
  4. 4.
    Blanz V, Vetter T (2002) Reconstructing the complete 3D shape of faces from partial information. Informationstechnik und Technische Informatik 44(6):295–302Google Scholar
  5. 5.
    De Senneville B, Ries M, Moonen C (2013) Real-time anticipation of organ displacement for MR-guidance of interventional procedures. In: IEEE international symposium on biomedical imaging, p 1420Google Scholar
  6. 6.
    Ehrhardt J, Werner R, Schmidt-Richberg A, Handels H (2011) Statistical modeling of 4D respiratory lung motion using diffeomorphic image registration. IEEE Trans Med Imag 30(2):251–265CrossRefGoogle Scholar
  7. 7.
    Eom J, Xu X, De S, Shi C (2010) Predictive modeling of lung motion over the entire respiratory cycle using measured pressure–volume data, 4DCT images, and finite-element analysis. Med Phys 37(8):4389–4401CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Hartkens T, Rueckert D, Schnabel J, Hawkes D, Hill D (2002) VTK CISG registration toolkit: an open source software package for affine and non-rigid registration of single-and multimodal 3D images. In: Bildverarbeitung für die Medizin, p 409Google Scholar
  9. 9.
    He T, Xue Z, Xie W, Wong S (2010) Online 4-D CT estimation for patient-specific respiratory motion based on real-time breathing signals. In: Medical image computing and computer-assisted intervention, p 392Google Scholar
  10. 10.
    Holbrook A, Ghanouni P, Santos J, Dumoulin C, Medan Y, Pauly K (2014) Respiration based steering for high intensity focused ultrasound liver ablation. Magn Reson Med 71(2):797–806CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    King A, Buerger C, Tsoumpas C, Marsden P, Schaeffter T (2012) Thoracic respiratory motion estimation from MRI using a statistical model and a 2-D image navigator. Med Image Anal 16:252–264CrossRefPubMedGoogle Scholar
  12. 12.
    Klinder T, Lorenz C, Ostermann J (2009) Free-breathing intra-and intersubject respiratory motion capturing, modeling, and prediction. In: Proceedings of SPIE, vol 7259. International Society for Optics and Photonics, p 72590TGoogle Scholar
  13. 13.
    Liu X, Oguz I, Pizer S, Mageras G (2010) Shape-correlated deformation statistics for respiratory motion prediction in 4D lung. In: Proceedings SPIE, vol 7625. International Society for Optics and PhotonicsGoogle Scholar
  14. 14.
    Low D, Parikh P, Lu W, Dempsey J, Wahab S, Hubenschmidt J, Nystrom M, Handoko M, Bradley J (2005) Novel breathing motion model for radiotherapy. Int J Radiat Oncol Biol Phys 63(3):921–929CrossRefPubMedGoogle Scholar
  15. 15.
    McClelland J, Hawkes D, Schaeffter T, King A (2013) Respiratory motion models: a review. Med Image Anal 17(1):19–42CrossRefPubMedGoogle Scholar
  16. 16.
    McClelland J, Hughes S, Modat M, Qureshi A, Ahmad S, Landau D, Ourselin S, Hawkes D (2011) Inter-fraction variations in respiratory motion models. Phys Med Biol 56:251–272CrossRefPubMedGoogle Scholar
  17. 17.
    Nguyen T, Moseley J, Dawson L, Jaffray D, Brock K (2009) Adapting liver motion models using a navigator channel technique. Med Phys 36(4):1061–1073CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Pernot M, Tanter M, Fink M (2004) 3-D real-time motion correction in high-intensity focused ultrasound therapy. Ultrasound Med Biol 30(9):1239–1249CrossRefPubMedGoogle Scholar
  19. 19.
    Preiswerk F, Arnold P, Fasel B, Cattin P (2011) A Bayesian framework for estimating respiratory liver motion from sparse measurements. In: Abdominal imaging, computational and clinical applications, p 207Google Scholar
  20. 20.
    Preiswerk F, De Luca V, Arnold P, Celicanin Z, Petrusca L, Tanner C, Bieri O, Salomir R, Cattin P (2014) Model-guided respiratory organ motion prediction of the liver from 2D ultrasound. Med Image Anal 18(5):740CrossRefPubMedGoogle Scholar
  21. 21.
    Ross J, Tranquebar R, Shanbhag D (2008) Real-time liver motion compensation for MRgFUS. In: Medical image computing and computer-assisted intervention, p 806Google Scholar
  22. 22.
    Roujol S, Benois-Pineau J, de Senneville B, Ries M, Quesson B, Moonen C (2012) Robust real-time-constrained estimation of respiratory motion for interventional MRI on mobile organs. IEEE Trans Inf Technol B 16(3):365–374CrossRefGoogle Scholar
  23. 23.
    Roujol S, Ries M, Moonen C, de Senneville B (2011) Robust real time motion estimation for MR-thermometry. In: IEEE international symposium on biomedical imaging, p 508Google Scholar
  24. 24.
    Rueckert D, Sonoda L, Hayes C, Hill D, Leach M, Hawkes D (1999) Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imag 18(8):712CrossRefGoogle Scholar
  25. 25.
    Samei G, Tanner C, Székely G (2012) Predicting liver motion using exemplar models. In: Abdominal imaging. Computational and clinical applications, p 147 (2012)Google Scholar
  26. 26.
    Schwenke M, Strehlow J, Haase S, Jenne J, Tanner C, Langø T, Loeve A, Karakitsios I, Xiao X, Levy Y, Sat G, Bezzi M, Braunewell S, Guenther M, Melzer A, Preusser T (2015) An integrated model-based software for fus in moving abdominal organs. Int J Hyperth 31(3):240–250CrossRefGoogle Scholar
  27. 27.
    de Senneville B, Mougenot C, Moonen C (2007) Real-time adaptive methods for treatment of mobile organs by MRI-controlled high-intensity focused ultrasound. Magn Reson Med 57(2):319CrossRefPubMedGoogle Scholar
  28. 28.
    de Senneville BD, Ries M, Maclair G, Moonen C (2011) MR-guided thermotherapy of abdominal organs using a robust PCA-based motion descriptor. IEEE Trans Med Imag 30(11):1987CrossRefGoogle Scholar
  29. 29.
    Tanner C, Boye D, Samei G, Székely G (2012) Review on 4D models for organ motion compensation. CR Rev Biom Eng 40(2):135CrossRefGoogle Scholar
  30. 30.
    Tanner C, Eppenhof K, Gelderblom J, Székely G (2014) Decision fusion for temporal prediction of respiratory liver motion. In: IEEE international symposium on biomedical imaging, p 698Google Scholar
  31. 31.
    Tanner C, Samei G, Székely G (2015) Robust exemplar model of respiratory liver motion and individualization using an additional breath-hold image. In: IEEE international symposium on biomedical imaging, p 1576 (2015)Google Scholar
  32. 32.
    Tanter M, Pernot M, Aubry JF, Montaldo G, Marquet F, Fink M (2007) Compensating for bone interfaces and respiratory motion in high-intensity focused ultrasound. Int J Hyperth 23(2):141–151CrossRefGoogle Scholar
  33. 33.
    Von Siebenthal M, Székely G, Gamper U, Boesiger P, Lomax A, Cattin P (2007) 4D MR imaging of respiratory organ motion and its variability. Phys Med Biol 52:1547CrossRefGoogle Scholar
  34. 34.
    Von Siebenthal M, Székely G, Lomax A, Cattin P (2007) Inter-subject modelling of liver deformation during radiation therapy. In: Medical image computing and computer-assisted intervention, p 659Google Scholar
  35. 35.
    Zadicario E, Rudich S, Hamarneh G, Cohen-Or D (2010) Image-based motion detection: using the concept of weighted directional descriptors. IEEE Eng Med Biol 29:87CrossRefGoogle Scholar
  36. 36.
    Zhang Q, Pevsner A, Hertanto A, Hu Y, Rosenzweig K, Ling C, Mageras G (2007) A patient-specific respiratory model of anatomical motion for radiation treatment planning. Med Phys 34(12):4772–4781CrossRefPubMedGoogle Scholar

Copyright information

© CARS 2016

Authors and Affiliations

  1. 1.Computer Vision LaboratoryETH ZürichZurichSwitzerland
  2. 2.GE Medical Systems IsraelHaifaIsrael
  3. 3.Institute for Medical Science and TechnologyUniversity of DundeeDundeeScotland, UK
  4. 4.Institute for Medical Image ComputingFraunhofer MEVISBremenGermany
  5. 5.School of MedicineUniversity of DundeeDundeeScotland, UK
  6. 6.Institute for Biomedical EngineeringUniversity and ETH ZürichZurichSwitzerland
  7. 7.Jacobs University BremenBremenGermany

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