Simulation of Range Imaging-based Estimation of Respiratory Lung Motion

Summary

Objectives: A major problem associated with the irradiation of thoracic and abdominal tumors is respiratory motion. In clinical practice, motion compensation approaches are frequently steered by low-dimensional breathing signals (e.g., spirometry) and patient-specific correspondence models, which are used to estimate the sought internal motion given a signal measurement. Recently, the use of multidimensional signals derived from range images of the moving skin surface has been proposed to better account for complex motion patterns. In this work, a simulation study is carried out to investigate the motion estimation accuracy of such multidimensional signals and the influence of noise, the signal dimensionality, and different sampling patterns (points, lines, regions).

Methods: A diffeomorphic correspondence modeling framework is employed to relate multidimensional breathing signals derived from simulated range images to internal motion patterns represented by diffeomorphic non-linear transformations. Furthermore, an automatic approach for the selection of optimal signal combinations/patterns within this framework is presented.

Results: This simulation study focuses on lung motion estimation and is based on 28 4D CT data sets. The results show that the use of multidimensional signals instead of one-dimensional signals significantly improves the motion estimation accuracy, which is, however, highly affected by noise. Only small differences exist between different multidimensional sampling patterns (lines and regions). Automatically determined optimal combinations of points and lines do not lead to accuracy improvements compared to results obtained by using all points or lines.

Conclusions: Our results show the potential of multidimensional breathing signals derived from range images for the model-based estimation of respiratory motion in radiation therapy.

Keywords

Respiratory motion - motion estimation - correspondence modeling - regression - image registration

• References

• 1 Keall PJ, Mageras GS, Balter JM. et al The management of respiratory motion in radiation oncology report of AAPM Task Group 76. Med Phys 2006; 33 (10) 3874-3900.
• 2 Werner R, Ehrhardt J, Frenzel T, Säring D, Lu W, Low D, Handels H. Motion artifact reducing reconstruction of 4D CT image data for the analysis of respiratory dynamics. Methods Inf Med 2007; 46 (03) 254-260.
• 3 Handels H, Ehrhardt J. Medical image computing for computer-supported diagnostics and therapy. Advances and perspectives. Methods Inf Med 2009; 48 (01) 11-17.
• 4 Kubo HD, Hill BC. Respiration gated radiotherapy treatment: A technical study. Phys Med Biol 1996; 41 (01) 83
• 5 Schweikard A, Glosser G, Bodduluri M, Murphy M, Adler JR. Robotic motion compensation for respiratory movement during radiosurgery. Comput Aided Surg 2000; 5: 263-277.
• 6 McClelland J, Hawkes D, Schaeffter T, King A. Respiratory motion models: A review. Medical Image Analysis 2012; 17 (01) 19-42.
• 7 Seppenwoolde Y, Shirato H, Kitamura K, Shimizu S, van Herk M, Lebesque JV. et al Precise and real-time measurement of 3D tumor motion in lung due to breathing and heartbeat, measured during radiotherapy. Int J Radiat Oncol Biol Phys 2002; 53 (04) 822-834.
• 8 Bauer S, Seitel A, Hofmann H. et al Real-Time Range Imaging in Health Care: A Survey. In: Time-of-Flight and Depth Imaging; LNCS Vol. Springer 2013; 8200: 228-254.
• 9 Fayad H, Pan T, Pradier O, Dimitris V. Patient specific respiratory motion modeling using a 3D patient’s external surface. Med Phys 2012; 39 (06) 3386-3395.
• 10 Wentz T, Fayad H, Bert J, Pradier O, Clement JF, Boussion N, Visvikis D. Accuracy of dynamic patient surface monitoring using a Time-of-Flight camera and B-splines modeling for respiratory motion characterization. Phys Med Biol 2012; 57 (13) 4175-4193.
• 11 Dong B, Graves YJ, Jia X, Jiang SB. Optimal surface marker locations for tumor motion estimation in lung cancer radiotherapy. Phys Med Biol 2012; 57: 8201-8215.
• 12 Wilms M, Werner R, Ehrhardt J, Schmidt-Richberg A, Blendowski M, Handels H. Surrogate-based Diffeomorphic Motion Estimation for Radiation Therapy: Comparison of Multivariate Regression Approaches. In: Proc. SPIE Medical Imaging 2013; I mage Processing; Vol 8669
• 13 Wilms M, Werner R, Ehrhardt J, Schmidt-Richberg A, Schlemmer HP, Handels H. Multivariate regression approaches for surrogate-based diffeomorphic estimation of respiratory motion in radiation therapy. Phys Med Biol 2014; 59 (05) 1147-1164.
• 14 Arsigny V, Commowick O, Pennec X, Ayache N. A Log-Euclidean Framework for Statistics on Diffeomorphisms. In: Proc. MICCAI 2006; LNCS Vol 4190: 924-931.
• 15 Ehrhardt J, Werner R, Schmidt-Richberg A, Handels H. Statistical Modeling of 4D Respiratory Lung Motion Using Diffeomorphic Image Registration. IEEE Trans Med Imag 2011; 30 (02) 251-265.
• 16 Schmidt-Richberg A, Ehrhardt J, Werner R. et al Diffeomorphic diffusion registration of lung CT images. In: Proc. Workshop Medical Image Analysis for the Clinic: A Grand Challenge - MICCAI 2010. 2010: 55-62.
• 17 Albert A. Regression and the Moore-Penrose Pseudoinverse. Academic Press; 1972
• 18 Olesen SM, Lyder S, Kraft D, Krüger N, Jessen JB. Real-time extraction of surface patches with associated uncertainties by means of Kinect cameras. J Real-Time Image Proc 2012; July. 2012: 1-14.
• 19 Castillo R, Castillo E, Guerra R. et al A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets. Phys Med Biol 2009; 54: 1849-1870.
• 20 Vandemeulebroucke J, Rit S, Kybic J, Clarysse P, Sarrut D. Spatiotemporal motion estimation for respiratory-correlated imaging of the lungs. Med Phys 2011; 38 (01) 166-178.
• 21 Kalousis A, Prados J, Hilario M. Stability of feature selection algorithms: a study on high-dimensional spaces. Knowl Inf Syst 2007; 12 (01) 95-116.