Material Density Mapping on Deformable 3D Models of Human Organs

Authors: Petru Manescu, Joseph Azencot, Michael Beuve, Hamid Ladjal, Jacques Saade, Jean-Michel Morreau, Philippe Giraud, Behzad Shariat

Abstract:

Organ motion, especially respiratory motion, is a technical challenge to radiation therapy planning and dosimetry. This motion induces displacements and deformation of the organ tissues within the irradiated region which need to be taken into account when simulating dose distribution during treatment. Finite element modeling (FEM) can provide a great insight into the mechanical behavior of the organs, since they are based on the biomechanical material properties, complex geometry of organs, and anatomical boundary conditions. In this paper we present an original approach that offers the possibility to combine image-based biomechanical models with particle transport simulations. We propose a new method to map material density information issued from CT images to deformable tetrahedral meshes. Based on the principle of mass conservation our method can correlate density variation of organ tissues with geometrical deformations during the different phases of the respiratory cycle. The first results are particularly encouraging, as local error quantification of density mapping on organ geometry and density variation with organ motion are performed to evaluate and validate our approach.

Keywords: Biomechanical simulation, dose distribution, image guided radiation therapy, organ motion, tetrahedral mesh, 4D-CT.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1077143

References:

[1] W. Schneider, T. Bortfeld, W. Schelgel, Correlation between CT numbers and tissue parameters needed for Monte Carlo simulations of clinical dose distributions, Phys. Med. Biol. 45; 459-478, 2000.
[2] J. Seco, G. Sharp, Z. Wu, D. Gierga, F. Buettner, H. Paganetti, Dosimetric impact of motion in free-breathing and gated lung radiotherapy: A 4D Monte Carlo study of intrafraction and interfraction effects, Med. Phys; 35(1): 356-366, 2008.
[3] M. Rosu, I.J. Chetty, J.M. Balter, M.L. Kessler, D.L. McShan, R.K. Ten Haken Dose reconstruction in deforming lung anatomy: dose grid size effects and clinical implications. Med. Phys;32:248795, 2005.
[4] D. Sarrut, Deformable registration for image-guided radiation therapy, Zeitschrift fur medizinische Physik 16 (4), 285-97, 2006.
[5] V. Boldea, G. Sharp, S. Jiang, D. Sarrut, 4D-CT lung motion estimation with deformable registration. Quantification of motion nonlinearity and hysteresis, Medical Physics 35 (3), 1008, 2008.
[6] S. Flampouri, S.B. Jiang, G.C. Sharp, J. Wolfgang, A.A. Patel, N.C. Choi, Estimation of the delivered patient dose in lung IMRT treatment based on deformable registration of 4D-CT data and Monte Carlo simulations. Phys. Med. Biol;51:276379, 2006.
[7] E. Heath, J. Sentjens. A direct voxel tracking method for fourdimensional Monte Carlo dose calculation in deforming anatomy. Med. Phys;33:43445, 2006.
[8] M. Velec, J. Moseley, B. Math, C. Eccles, T. Craig, M. Sharpe, L. Dawson, K. Brock, Effect of breathing motion on radiotherapy dose accumulation in the abdomen using deformable registration. Int. J Radiation Oncology Biol. Phys., Vol 80, No. 1, 265-272, 2011.
[9] J. Saade, A.L. Didier, P.F. Villard, R. Buttin, J.M. Moreau, M. Beuve, B. Shariat, A preliminary study for biomechanical model of the respiratpry system, VISAPP, 2010.
[10] A. Al-Mayah , J. Moseley , M. Velec, K. Brock , Toward efficient biomechanical-based deformable image registration of lungs for imageguided radiotherapy, Phys. Med. Biol. volume 56, number 15, 56 4701, 2011.
[11] M. Velec, J. Moseley, B. Math, T. Craig, , L. Dawson, K. Brock, Accumulated dose in liver stereotactic body radiotherapy:positioning, breathing and deformation effects. Int. J Radiation Oncology Biol. Phys., Vol 80, No. 1, 1-9, 2011.
[12] K. Brock , D. McShan , R. Ten Haken , S. Hollister , L. Dawson , J. Balter , Inclusion of organ deformation in dose calculations, Medical Physics , Vol. 30 (3), 290-295, 2003.
[13] A. Bhatt , R. Wakhedhar , Reverse Engineering of Human Body: A Bspline based Heterogeneous Modeling Approach, Computer-Aided Design and Applications, www.cadanda.com, 2008.
[14] M. Kalos , P. Whitlock , Monte Carlo Methods, Wiley, New York, 1986.
[15] C. Rocchini , P. Cignoni , Generating random points in a tetrahedron, Journal of Graphics Tools, Vol. 5, 200, 2001.
[16] T. Yoo , MJ. Ackerman , WE. Lorensen , Engineering and algorithm design for an image processing Api: a techiincal report on ITK - the Insight Toolkit, Stud Health Technol Inform 85: 586-92, http://www.itk.org, 2002.
[17] W. Schroeder , K. Martin , B. Lorensen , The Visualization Toolkit, ISBN 978-1930934191, http://www.vtk.org, 2006.
[18] P. Yushkevich , J. Piven , H. Hazlett , R. Smith , S. Ho , J. Gee , G. Gerig , User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability, Neuroimage 31(3);1116-28, 2006.
[19] M. Attene , B. Falcidieno , ReMesh: An interactive Environment to Edit and Repair Triangle Meshes, Procs of Shape Modeling International (SMI -06), IEEE C.S Press; 271-276, 2006.
[20] Z. Zhang , Iterative Point Matching for Registration of Free-form Curves, International Journal of Computer Vision, 13:2, 119-152, 1994.