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Erschienen in: International Journal of Computer Assisted Radiology and Surgery 6/2016

01.06.2016 | Original Article

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

verfasst von: C. Tanner, Y. Zur, K. French, G. Samei, J. Strehlow, G. Sat, H. McLeod, G. Houston, S. Kozerke, G. Székely, A. Melzer, T. Preusser

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 6/2016

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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.

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Literatur
1.
Zurück zum Zitat Ahrendt P (2005) The multivariate Gaussian probability distribution. Tech. rep Ahrendt P (2005) The multivariate Gaussian probability distribution. Tech. rep
2.
Zurück zum Zitat 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 623–630 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 623–630
3.
Zurück zum Zitat 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 51:4147CrossRefPubMed 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 51:4147CrossRefPubMed
4.
Zurück zum Zitat Blanz V, Vetter T (2002) Reconstructing the complete 3D shape of faces from partial information. Informationstechnik und Technische Informatik 44(6):295–302 Blanz V, Vetter T (2002) Reconstructing the complete 3D shape of faces from partial information. Informationstechnik und Technische Informatik 44(6):295–302
5.
Zurück zum Zitat 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 1420 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 1420
6.
Zurück zum Zitat 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):251–265CrossRef 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):251–265CrossRef
7.
Zurück zum Zitat 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):4389–4401CrossRefPubMedPubMedCentral 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):4389–4401CrossRefPubMedPubMedCentral
8.
Zurück zum Zitat 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 409 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 409
9.
Zurück zum Zitat 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 392 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 392
10.
Zurück zum Zitat 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):797–806CrossRefPubMedPubMedCentral 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):797–806CrossRefPubMedPubMedCentral
11.
Zurück zum Zitat 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 16:252–264CrossRefPubMed 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 16:252–264CrossRefPubMed
12.
Zurück zum Zitat 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 72590T 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 72590T
13.
Zurück zum Zitat 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 Photonics 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 Photonics
14.
Zurück zum Zitat 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):921–929CrossRefPubMed 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):921–929CrossRefPubMed
15.
Zurück zum Zitat McClelland J, Hawkes D, Schaeffter T, King A (2013) Respiratory motion models: a review. Med Image Anal 17(1):19–42CrossRefPubMed McClelland J, Hawkes D, Schaeffter T, King A (2013) Respiratory motion models: a review. Med Image Anal 17(1):19–42CrossRefPubMed
16.
Zurück zum Zitat 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 56:251–272CrossRefPubMed 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 56:251–272CrossRefPubMed
17.
Zurück zum Zitat Nguyen T, Moseley J, Dawson L, Jaffray D, Brock K (2009) Adapting liver motion models using a navigator channel technique. Med Phys 36(4):1061–1073CrossRefPubMedPubMedCentral Nguyen T, Moseley J, Dawson L, Jaffray D, Brock K (2009) Adapting liver motion models using a navigator channel technique. Med Phys 36(4):1061–1073CrossRefPubMedPubMedCentral
18.
Zurück zum Zitat Pernot M, Tanter M, Fink M (2004) 3-D real-time motion correction in high-intensity focused ultrasound therapy. Ultrasound Med Biol 30(9):1239–1249CrossRefPubMed Pernot M, Tanter M, Fink M (2004) 3-D real-time motion correction in high-intensity focused ultrasound therapy. Ultrasound Med Biol 30(9):1239–1249CrossRefPubMed
19.
Zurück zum Zitat 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 207 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 207
20.
Zurück zum Zitat 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):740CrossRefPubMed 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):740CrossRefPubMed
21.
Zurück zum Zitat Ross J, Tranquebar R, Shanbhag D (2008) Real-time liver motion compensation for MRgFUS. In: Medical image computing and computer-assisted intervention, p 806 Ross J, Tranquebar R, Shanbhag D (2008) Real-time liver motion compensation for MRgFUS. In: Medical image computing and computer-assisted intervention, p 806
22.
Zurück zum Zitat 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):365–374CrossRef 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):365–374CrossRef
23.
Zurück zum Zitat 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 508 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 508
24.
Zurück zum Zitat 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):712CrossRef 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):712CrossRef
25.
Zurück zum Zitat Samei G, Tanner C, Székely G (2012) Predicting liver motion using exemplar models. In: Abdominal imaging. Computational and clinical applications, p 147 (2012) Samei G, Tanner C, Székely G (2012) Predicting liver motion using exemplar models. In: Abdominal imaging. Computational and clinical applications, p 147 (2012)
26.
Zurück zum Zitat 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):240–250CrossRef 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):240–250CrossRef
27.
Zurück zum Zitat 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):319CrossRefPubMed 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):319CrossRefPubMed
28.
Zurück zum Zitat 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):1987CrossRef 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):1987CrossRef
29.
Zurück zum Zitat Tanner C, Boye D, Samei G, Székely G (2012) Review on 4D models for organ motion compensation. CR Rev Biom Eng 40(2):135CrossRef Tanner C, Boye D, Samei G, Székely G (2012) Review on 4D models for organ motion compensation. CR Rev Biom Eng 40(2):135CrossRef
30.
Zurück zum Zitat 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 698 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 698
31.
Zurück zum Zitat 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) 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)
32.
Zurück zum Zitat 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):141–151CrossRef 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):141–151CrossRef
33.
Zurück zum Zitat 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 52:1547CrossRef 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 52:1547CrossRef
34.
Zurück zum Zitat 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 659 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 659
35.
Zurück zum Zitat 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 29:87CrossRef 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 29:87CrossRef
36.
Zurück zum Zitat 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):4772–4781CrossRefPubMed 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):4772–4781CrossRefPubMed
Metadaten
Titel
In vivo validation of spatio-temporal liver motion prediction from motion tracked on MR thermometry images
verfasst von
C. Tanner
Y. Zur
K. French
G. Samei
J. Strehlow
G. Sat
H. McLeod
G. Houston
S. Kozerke
G. Székely
A. Melzer
T. Preusser
Publikationsdatum
01.06.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Computer Assisted Radiology and Surgery / Ausgabe 6/2016
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-016-1405-4

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