MARIO : modélisation de l'anatomie normale et pathologique pour le recalage non linéaire entre images TDM et TEP en oncologie

The MARIO project deals with the problem of 3D registration o f Computed Tomog- raphy (CT) images (at two different instants of the breathin g cycle) and Positon Emission To- mography (PET) images of thoracic regions. In order to guara ntee physiologically plausible deformations, we present a novel method to incorporate a bre athing model in a non-linear registration procedure. Our registration method is based o the segmentation of anatomical structures and potential tumors, and on an automatic select ion of landmark points based on the curvature of the lung surface. The rigidity of the tumors is p reserved during the registration and constraints on the heart are included, while guaranteei ng a continuous deformation. Re- sults on one normal case and four pathological cases demonst rate the interest of this method to

[1]  Geoffrey G. Zhang,et al.  Elastic image mapping for 4-D dose estimation in thoracic radiotherapy. , 2005, Radiation protection dosimetry.

[2]  Jürgen Weese,et al.  Landmark-based elastic registration using approximating thin-plate splines , 2001, IEEE Transactions on Medical Imaging.

[3]  Cameron J. Ritchie,et al.  Respiratory compensation in projection imaging using a magnification and displacement model , 1996, IEEE Trans. Medical Imaging.

[4]  A. Ardeshir Goshtasby,et al.  Image Registration Methods , 2012 .

[5]  J. Adler,et al.  Robotic Motion Compensation for Respiratory Movement during Radiosurgery , 2000, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

[6]  Pierre Baconnier,et al.  Physically-based model for simulating the human trunk respiration movements , 1997, CVRMed.

[7]  Marcel Breeuwer,et al.  Myocardial Delineation via Registration in a Polar Coordinate System , 2002, MICCAI.

[8]  Jay B. West,et al.  Hybrid point-and-intensity-based deformable registration for abdominal CT images , 2005, SPIE Medical Imaging.

[9]  Antonio Moreno Ingelmo Recalage non-linéaire d'images TEP et CT du thorax pour la caractérisation des tumeurs : application à la radiothérapie , 2007 .

[10]  I. Bloch,et al.  Combining a breathing model and tumor-specific rigidity constraints for registration of CT-PET thoracic data , 2008, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

[11]  Haiying Liu,et al.  Using Points and Surfaces to Improve Voxel-Based Non-rigid Registration , 2002, MICCAI.

[12]  Victor B. Zordan,et al.  Breathe easy: Model and control of human respiration for computer animation , 2006, Graph. Model..

[13]  R. Shekhar,et al.  Automated 3-dimensional elastic registration of whole-body PET and CT from separate or combined scanners. , 2005, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[14]  David Sarrut,et al.  Nonrigid registration method to assess reproducibility of breath-holding with ABC in lung cancer. , 2004, International journal of radiation oncology, biology, physics.

[15]  Margrit Betke,et al.  Landmark detection in the chest and registration of lung surfaces with an application to nodule registration , 2003, Medical Image Anal..

[16]  Li Yuan-yuan A SURVEY OF MEDICAL IMAGE REGISTRATION , 2006 .

[17]  Azriel Rosenfeld,et al.  Digital geometry - geometric methods for digital picture analysis , 2004 .

[18]  Isabelle Bloch,et al.  Integration of fuzzy spatial relations in deformable models - Application to brain MRI segmentation , 2006, Pattern Recognit..

[19]  Olivier Ecabert,et al.  Automatic whole heart segmentation in CT images: method and validation , 2007, SPIE Medical Imaging.

[20]  Josien P. W. Pluim,et al.  Image Registration , 2003, IEEE Trans. Medical Imaging.

[21]  J. Weese,et al.  Towards automatic full heart segmentation in computed-tomography images , 2005, Computers in Cardiology, 2005.

[22]  Isabelle Bloch,et al.  Introduction d'un modèle de respiration dans une méthode de recalage à partir de points d'intérêt d'images TEP et TDM du poumon Using a breathing model for landmark-based CT-PET lung registration , 2008 .

[23]  U Narusawa,et al.  General characteristics of the sigmoidal model equation representing quasi-static pulmonary P-V curves. , 2001, Journal of applied physiology.

[24]  B. Simon,et al.  A comprehensive equation for the pulmonary pressure-volume curve. , 1998, Journal of applied physiology.

[25]  P. H. Gregson Automatic segmentation of the heart in 3D MR images , 1994, 1994 Proceedings of Canadian Conference on Electrical and Computer Engineering.

[26]  Isabelle Bloch,et al.  Introducing Shape Constraint Via Legendre Moments in a Variational Framework for Cardiac Segmentation on Non-contrast CT Images , 2010, VISAPP.

[27]  Gareth Funka-Lea,et al.  Automatic heart isolation for CT coronary visualization using graph-cuts , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[28]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  Marie-Pierre Jolly,et al.  Automatic Segmentation of the Left Ventricle in Cardiac MR and CT Images , 2006, International Journal of Computer Vision.

[30]  David Sarrut,et al.  Extraction du signal respiratoire à partir de projections cone-beam pour l'imagerie TDM 4D Extraction of the respiratory signal from cone-beam projections for 4D CT imaging , 2006 .

[31]  James C. Gee,et al.  Towards a model of lung biomechanics: pulmonary kinematics via registration of serial lung images , 2005, Medical Image Anal..

[32]  J. McClelland,et al.  A continuous 4D motion model from multiple respiratory cycles for use in lung radiotherapy. , 2006, Medical physics.

[33]  Jyrki Lötjönen,et al.  Evaluation of cardiac PET-MRI registration methods using a numerical breathing phantom , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[34]  W. Segars,et al.  Study of the efficacy of respiratory gating in myocardial SPECT using the new 4-D NCAT phantom , 2001 .

[35]  E Yorke,et al.  Four-dimensional (4D) PET/CT imaging of the thorax. , 2004, Medical physics.

[36]  Isabelle Bloch,et al.  Using anatomical knowledge expressed as fuzzy constraints to segment the heart in CT images , 2008, Pattern Recognit..

[37]  Jannick P. Rolland,et al.  Modeling Real-Time 3-D Lung Deformations for Medical Visualization , 2008, IEEE Transactions on Information Technology in Biomedicine.

[38]  Johannes H A M Kaanders,et al.  Correction of an image size difference between positron emission tomography (PET) and computed tomography (CT) improves image fusion of dedicated PET and CT , 2006, Nuclear medicine communications.

[39]  Heinz Handels,et al.  A Variational Approach for Combined Segmentation and Estimation of Respiratory Motion in Temporal Image Sequences , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[40]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[41]  Roberto Marcondes Cesar Junior,et al.  On the ternary spatial relation "Between" , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[42]  Paul F. Whelan,et al.  Automatic segmentation of the left ventricle cavity and myocardium in MRI data , 2006, Comput. Biol. Medicine.

[43]  Marcel Breeuwer,et al.  Myocardial delineation via registration in a polar coordinate system1 , 2003 .

[44]  Torsten Rohlfing,et al.  Modeling liver motion and deformation during the respiratory cycle using intensity-based free-form registration of gated MR images , 2001, SPIE Medical Imaging.

[45]  Boudewijn P. F. Lelieveldt,et al.  Anatomical model matching with fuzzy implicit surfaces for segmentation of thoracic volume scans , 1999, IEEE Transactions on Medical Imaging.

[46]  W. O'Dell,et al.  Modeling liver motion and deformation during the respiratory cycle using intensity-based nonrigid registration of gated MR images. , 2004, Medical physics.

[47]  Fred L. Bookstein,et al.  Principal Warps: Thin-Plate Splines and the Decomposition of Deformations , 1989, IEEE Trans. Pattern Anal. Mach. Intell..