Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-23T23:53:42.887Z Has data issue: false hasContentIssue false
  • English
  • Français

Perspective de la plate-forme NEMOSIS dans lecadre d’une réduction de doses en imagerie

Published online by Cambridge University Press:  09 November 2012

R. Laurent ,
R. Gschwind ,
M. Salomon ,
J. Henriet  and
L. Makovicka
R. Laurent
Affiliation:
IRMA/LCPR-AC/Chrono-Environnement, UMR 6249 CNRS, Université de Franche-Comté, BP 71427, 25211 Montbéliard Cedex, France
R. Gschwind
Affiliation:
IRMA/LCPR-AC/Chrono-Environnement, UMR 6249 CNRS, Université de Franche-Comté, BP 71427, 25211 Montbéliard Cedex, France
M. Salomon
Affiliation:
AND/DISC/FEMTO-ST, UMR 6174 CNRS, Université de Franche-Comté, BP 527, 90016 Belfort Cedex, France
J. Henriet
Affiliation:
IRMA/LCPR-AC/Chrono-Environnement, UMR 6249 CNRS, Université de Franche-Comté, BP 71427, 25211 Montbéliard Cedex, France
L. Makovicka
Affiliation:
IRMA/LCPR-AC/Chrono-Environnement, UMR 6249 CNRS, Université de Franche-Comté, BP 71427, 25211 Montbéliard Cedex, France
Get access

Abstract

L’acquisition du mouvement est de plus en plus souventeffectuée pour améliorer la balistique des traitements en radiothérapieexterne. Cependant, elle est source d’une exposition supplémentairepour le patient. Le développement de la plate-forme de simulationnumérique NEMOSIS (NEural NEtwork MOtion SImulation System)ouvre la voie à l’optimisation de la dose en imagerie. Elle permetde générer un mouvement pulmonaire localisé et personnalisé à partirdu modèle 3D du patient. Pour 3 patients test, 5 à 6 points anatomiquesont été simulés puis comparés aux tracés du radiothérapeute. Dansle cas le plus défavorable, les résultats ont montré une précisionmoyennée sur l’ensemble des points d’un patient et sur toutes lesphases d’environ 3 mm avec une incertitude élargie de tracé égaleà 1,5 mm (intervalle de confiance de 95 %) et une incertitude maximalede phase atteignant 6,53 mm. Une autre étude comparant les GTV (Gross Tumor Volume) d’un radiothérapeute et ceuxcalculés par NEMOSIS a été également menée. Un indice de Dice stipulant unecorrespondance minimale de 0,80 a été calculé entre les deux typesde volumes. Ces résultats font de NEMOSIS un outil très prometteuren tant qu’alternative aux imageries irradiantes.

Keywords

lung motionnumerical simulationdose optimizationimaging
Type
Research Article
Information
Radioprotection , Volume 47 , Issue 4 , October 2012 , pp. 599 - 617
Copyright
© EDP Sciences, 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Références

Boldea V. (2006) Intégration de la respiration en radiothérapie : apport du recalage déformable d'images, Thèse de doctorat en informatique, Université Lumière Lyon 2.
Davies, S.C., Hill, A.L., Holmes, R.B., Halliwell, M., Jackson, P.C. (1994) Ultrasound quantitation of respiratory organ motion in the upper abdomen, Br. J. Radiol. 67 (803), 1096-1102.Google ScholarPubMed
Ehrhardt, J., Werner, R., Säring, D., Frenzel, T., Lu, W., Low, D.A., Handels, H. (2007) An optical flow based method for improved reconstruction of 4DCT data sets acquired during free breathing, Medical Physics 34 (2), 711-721.Google ScholarPubMed
Eom, J., Xu, X.G., 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), 4389-4400.Google ScholarPubMed
Ford, E.C., Mageras, G.S., Yorke, E., Rosenzweig, K.E., Wagman, R., Ling, C.C. (2002) Evaluation of respiratory movement during gated radiotherapy using film and electronic portal imaging, Int. J. Radiat. Oncol. Biol. Phys. 52 (2), 522-531.Google ScholarPubMed
Giraud, P., De Rycke, Y., Dubray, B., Helfre, S., Voican, D., Guo, L., Rosenwald, J.C., Keraudy, K., Housset, M., Touboul, E., Cosset, J.M. (2001) Conformal radiotherapy (CRT) planning for lung cancer: analysis of intrathoracic organ motion during extreme phases of breathing, Int. J. Radiat. Oncol. Biol. Phys. 51 (4), 1081-1092.Google Scholar
Hanley, J., Debois, M.M., Mah, D., Mageras, G.S., Raben, A., Rosenzweig, K.E., Mychalczak, B., Schwartz, L.H., Gloeggler, P.J., Lutz, W., Ling, C.C., Leibel, S.A., Fuks, Z., Kutcher, G.J. (1999) Deep inspiration breath-hold technique for lung tumors: the potential value of target immobilization and reduced lung density in dose escalation, Int. J. Radiat. Oncol. Biol. Phys. 45 (3), 603-611.Google ScholarPubMed
Hostettler A., Nicolau S.A., Forest C., Soler L., Rémond Y. (2006) Real-time simulation of organ motions induced by breathing: First evaluation on patient data. In: ISBMS, Vol. 4072 of LNCS, pp. 9-18.
Hostettler A., Nicolau S., Soler L., Rémond Y., Marescaux J. (2008) A real-time predictive simulation of abdominal organ positions induced by free breathing. In: Biomedical Simulation, Springer Berlin / Heidelberg (Bello F., Edwards P., Eds), Vol. 5104 of LNCS, pp. 89-97.
Johnston, E., Diehn, M., Murphy, J.D., Loo, B.W. Jr, Maxim, P.G. (2011) Reducing 4DCT artifacts using optimized sorting based on anatomic similarity, Med. Phys. 38 (5), 2424-2429.Google ScholarPubMed
Laurent, R., Henriet, J., Gschwind, R., Makovicka, L. (2010) A morphing technique applied to lung motions in radiotherapy: preliminary results, Acta Polytechnica 50 (6), 57-65.Google Scholar
Laurent, R., Henriet, J., Salomon, M., Sauget, M., Nguyen, F., Gschwind, R., Makovicka, L. (2011) Simulation of lung motions using an artificial neural network, Cancer Radiothérapie 15 (2), 123-129.Google ScholarPubMed
Laurent, R., Henriet, J., Salomon, M., Sauget, M., Gschwind, R., Makovicka, L. (2012) Respiratory lung motion using an artificial neural network, Neural Comput. Applic. 21 (5), 929-934, DOI:10.1007/s00521-011-0727-y.Google Scholar
Liu, H.H., Balter, P., Tutt, T., Choi, B., Zhang, J., Wang, C., Chi, M., Luo, D., Pan, T., Hunjan, S., Starkschall, G., Rosen, I., Prado, K., Liao, Z., Chang, J., Komaki, R., Cox, J.D., Mohan, R., Dong, L. (2007) Assessing respiration-induced tumor motion and internal target volume using four-dimensional computed tomography for radiotherapy of lung cancer, Int. J. Radiat. Oncol. Biol. Phys. 68 (2), 531-540.Google ScholarPubMed
Louie, A.V., Rodrigues, G., Olsthoorn, J., Palma, D., Yu, E., Yaremko, B., Ahmad, B., Aivas, I., Gaede, S. (2010) Inter-observer and intra-observer reliability for lung cancer target volume delineation in the 4D-CT era, Radiother. Oncol. 95 (2), 166-171.Google ScholarPubMed
Low, D.A., Parikh, P.J., Lu, W., Dempsey, J.F., Wahab, S.H., Hubenschmidt, J.P., Nystrom, M.M., Handoko, M., Bradley, J.D. (2005) Novel breathing motion model for radiotherapy, Int. J. Radiat. Oncol. Biol. Phys 63 (3), 921-929.Google ScholarPubMed
Low, D.A., Nystrom, M., Kalinin, E., Parikh, P., Dempsey, J.F., Bradley, J.D., Mutic, S., Wahab, S.H., Islam, T., Christensen, G., Politte, D.G., Whiting, B.R. (2003) A method for the reconstruction of four-dimensional synchronized CT scans acquired during free breathing, Med. Phys. 30 (6), 1254-1263.Google ScholarPubMed
Murphy, M.J., Martin, D., Whyte, R., Hai, J., Ozhasoglu, C., Le, Q.T. (2002) The effectiveness of breath-holding to stabilize lung and pancreas tumors during radiosurgery, Int. J. Radiat. Oncol. Biol. Phys. 53 (2), 475-482.Google ScholarPubMed
Persson, G.F., Nygaard, D.E., Brink, C., Jahn, J.W., Rosenschöld, P.M., Specht, L., Korreman, S.S. (2010) Deviations in delineated GTV caused by artefacts in 4DCT, Radiother. Oncol. 96 (1), 61-66.Google ScholarPubMed
Redmond, K.J., Song, D.Y., Fox, J.L., Zhou, J., Rosenzweig, C.N., Ford, E. (2009) Respiratory motion changes of lung tumors over the course of radiation therapy based on respiration-correlated four-dimensional computed tomography scans, Int. J. Radiat. Oncol. Biol. Phys. 75 (5), 1605-1612.Google ScholarPubMed
Rietzel, E., Pan, T., Chen, G.T.Y. (2005) Four-dimensional computed tomography: Image formation and clinical protocol, Med. Phys. 32 (4), 874-889.Google Scholar
Sarker, J., Chu, A., Mui, K., Wolfgang, J.A., Hirsch, A.E., Chen, G.T.Y., Sharp, G.C. (2010) Variations in tumor size and position due to irregular breathing in 4D-CT: A simulation study, Med. Phys. 37, 3, 1254-1260.Google ScholarPubMed
Simon L. (2006) Etude comparative et mise en œuvre clinique de deux systèmes de radiothérapie asservie à la respiration, Thèse de doctorat de physique radiologique et médicale, Université de Paris XI - Faculté de Médecine de Paris-Sud.
Vandemeulebroucke J., Sarrut D., Clarysse P. (2007) The popi-model, a point-validated pixel-based breathing thorax model. In: XVth International Conference on the Use of Computers in Radiation Therapy (ICCR 2007), Toronto, Canada, 4-7 June.
Vandemeulebroucke, J., Rit, S., Kybic, J., Clarysse, P., Sarrut, D. (2011) Spatiotemporal motion estimation for respiratory-correlated imaging of the lungs, Med. Phys. 38 (1), 166-178.Google ScholarPubMed
Van de Steene, J., Van den Heuvel, F., Bel, A., Verellen, D., De Mey, J., Noppen, M., De Beukeleer, M., Storme, G. (1998) Electronic portal imaging with on-line correction of setup error in thoracic irradiation: clinical evaluation, Int. J. Radiat. Oncol. Biol. Phys. 40 (4), 967-976.Google Scholar
Villard P.F. (2006) Simulation du Mouvement Pulmonaire pour un Traitement Oncologique, Thèse de doctorat en informatique n°165-2006, Université Claude Bernard.
Yamamoto, T., Langner, U., Loo, B.W. Jr, Shen, J., Keall, P.J. (2008) Retrospective analysis of artifacts in four-dimensional CT images of 50 abdominal and thoracic radiotherapy patients, Int. J. Rad. Oncol. Biol. Phys. 72 (4), 1250-1258.Google Scholar
Yang, D., Lu, W., Low, D.A., Deasy, J.O., Hope, A.J., El Naqa, I. (2008) 4DCT motion estimation using deformable image registration and 5D respiratory motion modeling, Med. Phys. 35 (10), 4577-4590.Google Scholar
Zeng, R., Fessler, J.A., Balter, J.M., Balter, P.A. (2008) Iterative sorting for four dimensional CT images based on internal anatomy motion, Med. Phys. 35 (3), 917-926.Google Scholar
Zhao, T., Lu, W., Yang, D., Mutic, S., Noel, C.E., Parikh, P.J., Bradley, J.D., Low, D.A. (2009) Characterization of free breathing patterns with 5D lung motion model, Med. Phys. 36 (11) , 5183-5189.Google Scholar