Simulation of minimally invasive vascular interventions for training purposes

References

• Lunderquist A, Ivancev K, Wallace S, Enge I, Laerum F, Kolbenstvedt AN. The acquisition of skills in interventional radiology by supervised training on animal models: a three-year multicenter experience. Cardiovascular and Interventional Radiology 1995;18(4):209–11.
   PubMed Web of Science ®Google Scholar
• Mori T, Hatano N, Maruyama S, Atomi Y. Significance of “hands-on training” in laparoscopic surgery. Surgical Endoscopy 1998;12:256–60.
   PubMed Web of Science ®Google Scholar
• Abdoulaev G, Cadeddu S, Delussu G, Donizelli M, Formaggia L, Giachetti A, Gobbetti E, Leone A, Manzi C, Pili P, Scheinine A, Tuveri M, Varone A, Veneziani A, Zanetti G, Zorcolo A. ViVa: The virtual vascular project. IEEE Trans Info Tech Biomed 1998;22(4):268–74.
   Google Scholar
• Hahn JK, Kaufman R, Winick AB, Carleton T, Park Y, Lindeman R, Oh KM, Al-Ghreimil N, Walsh RJ, Loew M, Sankar S. Training environment for inferior vena caval filter placement. In: Westwood JD, Hoffman HM, Weghorst SJ, Stredney D, editors. Medicine Meets Virtual Reality: Studies in Health Technology and Informatics (Proceedings of MMVR 1998). Amsterdam: IOS Press, 1998. p 291–7.
   Google Scholar
• Zorcolo A, Gobbetti E, Zanetti G, Tuveri M. A volumetric environment for catheter insertion simulation. In: van Liere R, Mulder J, editors. Proceedings of 6th Eurographics Workshop on Virtual Environments (EGVE 2000). Lecture Notes in Computer Science. Berlin, Springer, 2000.
   Google Scholar
• Zorcolo A, Gobetti E, Zanetti G, Tuveri M. Catheter insertion simulation with co-registered direct volume rendering and haptic feedback. In: Westwood JD, Hoffman HM, Mogel GT, Robb A, Stredney D, editors. Medicine Meets Virtual Reality: Studies in Health Technology and Informatics (Proceedings ofMMVR 2000). Amsterdam: IOS Press, 2000. p 96–8.
   Google Scholar
• Anderson J, Chui CK, Cai Y, Wang Y, Li Z, Ma X, Nowinski W, Solaiyappan M, Murphy K, Gailloud P, Venbrux A. Virtual reality training in interventional radiology: the John Hopkins and Kent Ridge Digital Laboratory experience. Seminars in Interventional Radiology 2002;19(2):179–85.
   Google Scholar
• Chui CK, Li Z, Anderson JH, Murphy K, Venbrux A, Ma X, Wang Z, Gailloud P, Cai Y, Wang Y, Nowinski WL. Training and pretreatment planning of interventional neuroradiology procedures—initial clinical validation. In: Westwood JD, Hoffman HM, Mogel GT, Robb A, Stredney D, editors. Medicine Meets Virtual Reality: Studies in Health Technology and Informatics, (Proceedings of MMVR 2002). Amsterdam: IOS Press, 2002. p 96–102.
   Google Scholar
• Li Z, Chui CK, Anderson JH, Chen X, Ma X, Hua W, Peng Q, Cai Y, Wang Y, Nowinski WL. Computer environment for interventional neuroradiology procedures. Simulation and Gaming 2001;32(3):404–19.
   Google Scholar
• Wang Y, Chui CK, Lim H, Cai Y, Mak K. Real-time interactive simulator for percutaneous coronary revascularization procedures. Comput Aided Surg 1998;3:211–27.
   PubMedGoogle Scholar
• Zienkewickz O, Taylor R. The Finite Element Method. New York: McGraw-Hill, 1987.
   Google Scholar
• Cotin S, Delingette H, Ayache N. Real-time elastic deformations Visualization Comput Graphics 1999;5(1):62–73.
   Google Scholar
• Cotin S, Dawson SL, Meglan D, Shaffer DW, Ferrell MA, Bardsley RS, Morgan FM, Nagano T, Nikom J, Sherman P, Walterman MT, Wendlandt J. ICTS, an interventional cardiology training system. In: Westwood JD, Hoffman HM, Mogel GT, Robb A, Stredney D, editors. Medicine Meets Virtual Reality: Studies in Health Technology and Informatics (Proceedings of MMVR 2000). Amsterdam: IOS Press, 2000. p 59–65.
   Google Scholar
• Dawson SL, Cotin S, Meglan D, Shaffer, DW, Ferrell MA. Designing a computer-based simulator for interventional cardiology training. Catheterization and Cardiovascular Interventions 2000;51(4):522–7.
   PubMed Web of Science ®Google Scholar
• Konings MK, van de Kraats EB, Alderliesten T, Niessen WJ. Analytical guide wire motion algorithm for simulation of endovascular interventions. Medical & Biological Engineering & Computing 2003;41:689–700.
   PubMed Web of Science ®Google Scholar
• Hogan N, Winters JM. Principles underlying movement organization: upper limb. In: Winters JM, Woo SL-Y, editors. Multiple Muscle Systems. Biomechanics and Movement Organization. New York: Springer, 1990. p 182–94.
   Google Scholar
• Hearn EJ. Mechanics of Materials. Part I (3rd edition). Oxford: Butterworth-Heinemann, 1997.
   Google Scholar
• Arfken G. Mathematical Methods for Physicists. Academic Press, Inc., 1985.
   Google Scholar
• Kemkers R, op de Beek J, Aerts H, Koppe R, Klotz E, Grass M, Moret J. First clinical application with use of a standard Philips C-arm system. In: Lemke HU, Vannier MW, Inamura K, Farman AG, editors. Computer Aided Radiology and Surgery. Proceedings of the 12th International Symposium and Exhibition (CARS ’98), Tokyo, June 1998. Amsterdam: Elsevier, 1998. p 182–7.
   Google Scholar
• Alderliesten T, Niessen WJ, Vincken KL, Maintz JBA, Jansen FE, van Nieuwenhuizen O, Viergever MA. Objective and reproducible segmentation and quantification of tuberous sclerosis lesions in FLAIR brain MR images. In: Proceedings of SPIE Medical Imaging 2001. SPIE vol. 4322; 2 (27).p 1509–18.
   Google Scholar
• Udupa JK, Samarasekera S. Fuzzy Connectedness and Object Definition: Theory, algorithms and applications in image segmentation. Graphical Models and Image Processing 1996;58(3):246–61.
   Google Scholar
• Alderliesten T, Konings MK, Niessen WJ. Simulation of guide wire propagation for minimally invasive vascular interventions. In: Dohi T, Kikinis R, editors. Proceedings of 5th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2002), Tokyo, Japan, September 2002. Lecture Notes in Computer Science 2489, Part II. Berlin: Springer, 2002. p 245–52.
   Google Scholar