000130830 001__ 130830
000130830 005__ 20190509132216.0
000130830 0247_ $$2doi$$a10.5075/epfl-thesis-4319
000130830 02470 $$2urn$$aurn:nbn:ch:bel-epfl-thesis4319-5
000130830 02471 $$2nebis$$a5718149
000130830 037__ $$aTHESIS
000130830 041__ $$aeng
000130830 088__ $$a4319
000130830 245__ $$aVR simulation of interventional radiology and microsurgery
000130830 269__ $$a2009
000130830 260__ $$bEPFL$$c2009$$aLausanne
000130830 300__ $$a132
000130830 336__ $$aTheses
000130830 520__ $$aCurrently trainees typically learn surgery techniques directly on mocks, animals or cadavers. For safety, cost and ethics reasons, one cannot try all possible strategies to achieve efficient learning. Virtual Reality (VR) based surgery training systems introduce a complementary alternative in surgery training and education, which will enable more effective and systematic training, provide objective assessment of technical competence, facilitate the teaching of rare cases, and enable to test the future surgeons. However, to be a useful training tool, surgery simulator must be visually and physically realistic. This dissertation investigates the training method and fast modeling algorithms for the purpose of surgery simulator. Different to conventional training approach, a strategy of using decomposition of complex surgery procedures into subtasks and using multisensory learning cues is proposed. Given the computational constraint of real-time simulation, several physics-based approaches of modeling rigid and deformable objects, (self-)collision detection and collision handling techniques are introduced and implemented. The developed algorithms are firstly tested in the simulation of interventional radiology (IR) procedures. The simulation environment allows to carry out the most common procedures: guidewire and catheter navigation, contrast dye injection to visualize the vessels, balloon angioplasty and stent placement. Moreover, visual details including the heartbeat as well as breathing are also considered. Finally, we present a VR based microsurgery simulator which applies and extends the above training strategy and algorithms. The training system demonstrates a complete vascular suturing procedure and a series of decomposed subtasks. Using a novel haptic forceps, a user can learn principle microsurgery skills such as grasping, suture placement, needle insertion and knot-tying.
000130830 6531_ $$avirtual reality
000130830 6531_ $$ahaptics
000130830 6531_ $$aphysics-based simulation
000130830 6531_ $$acollision detection
000130830 6531_ $$atraining approach
000130830 6531_ $$ainterventional radiology
000130830 6531_ $$amicrosurgery
000130830 6531_ $$aréalité virtuelle
000130830 6531_ $$ahaptique
000130830 6531_ $$asimulation physique
000130830 6531_ $$adétecter des collisions
000130830 6531_ $$améthode d'apprentissage
000130830 6531_ $$aradiologie interventionnelle
000130830 6531_ $$amicrochirurgie
000130830 700__ $$aWang, Fei
000130830 720_2 $$aBleuler, Hannes$$edir.$$g104561$$0240027
000130830 8564_ $$uhttps://infoscience.epfl.ch/record/130830/files/EPFL_TH4319.pdf$$zTexte intégral / Full text$$s6386643$$yTexte intégral / Full text
000130830 909C0 $$0252016$$pLSRO
000130830 909CO $$pSTI$$pthesis$$pthesis-bn2018$$pDOI$$ooai:infoscience.tind.io:130830$$qDOI2$$qGLOBAL_SET
000130830 918__ $$dEDPR$$cIMT$$aSTI
000130830 919__ $$aLSRO1
000130830 920__ $$b2009
000130830 970__ $$a4319/THESES
000130830 973__ $$sPUBLISHED$$aEPFL
000130830 980__ $$aTHESIS