In vivo validation of spatio-temporal liver motion prediction from motion tracked on MR thermometry images
- 252 Downloads
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.
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.
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.
The study shows that tracking liver vessels on MR images, which are also used for MR thermometry, is a viable approach.
KeywordsFocused ultrasound Respiration Motion prediction Tracking
This study was funded by the EU’s 7th Framework Program (FP7/2007-2013) under Grant Agreement Nos. 270186 (FUSIMO) and 611889 (TRANS-FUSIMO).
Compliance with ethical standards
Conflict of interest
Y.Z. and G.Sat are employed by GE. A.M. acknowledges research collaborations with GE, InSightec and IBSmm, and support from the Northern Research Partnership. All other authors declare no conflict of interest.
Research involving human participants
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This study does not contain patient data.
Informed consent was obtained from all individual participants included in the study.
- 1.Ahrendt P (2005) The multivariate Gaussian probability distribution. Tech. repGoogle Scholar
- 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
- 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.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
- 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.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
- 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.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
- 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
- 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
- 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
- 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
- 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.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
- 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