PURPOSE: Patient-specific surgical simulation imposes both practical and technical challenges. We propose a segmentation-free, modeling-free framework that creates medical volumetric models for intuitive volume deformation and manipulation in patient-specific surgical simulation. METHODS: The proposed framework creates a volumetric model based upon a new form of mesh structure, a Volume Proxy Mesh (VPM). The model can be generated in two phases: the vertex placement phase and mesh improvement phase. Vertices of a VPM are assigned to an initial location by curvature-based vertex placement method, and followed by mesh improvement performed by Particle Swarm Optimization (PSO). RESULTS: The framework is applied to several kidney CT volume data. Using the framework, the resulting models are closely tailored to the detailed features of the datasets. Moreover, the resulting VPM meshes can support broader spectrum deformation between the manipulated organ and its surrounding tissues. Progress in the mesh quality of the final mesh also shows that PSO is feasible for mesh improvement. CONCLUSION: The framework was applied to several kidney CT volume datasets. Using the framework, the resulting models are closely tailored to the detailed features of the datasets. Moreover, the resulting VPM meshes can support broader spectrum deformation between the manipulated organ and its surrounding tissues. Evaluation of final mesh quality shows that PSO is feasible for mesh improvement.
PURPOSE:Patient-specific surgical simulation imposes both practical and technical challenges. We propose a segmentation-free, modeling-free framework that creates medical volumetric models for intuitive volume deformation and manipulation in patient-specific surgical simulation. METHODS: The proposed framework creates a volumetric model based upon a new form of mesh structure, a Volume Proxy Mesh (VPM). The model can be generated in two phases: the vertex placement phase and mesh improvement phase. Vertices of a VPM are assigned to an initial location by curvature-based vertex placement method, and followed by mesh improvement performed by Particle Swarm Optimization (PSO). RESULTS: The framework is applied to several kidney CT volume data. Using the framework, the resulting models are closely tailored to the detailed features of the datasets. Moreover, the resulting VPM meshes can support broader spectrum deformation between the manipulated organ and its surrounding tissues. Progress in the mesh quality of the final mesh also shows that PSO is feasible for mesh improvement. CONCLUSION: The framework was applied to several kidney CT volume datasets. Using the framework, the resulting models are closely tailored to the detailed features of the datasets. Moreover, the resulting VPM meshes can support broader spectrum deformation between the manipulated organ and its surrounding tissues. Evaluation of final mesh quality shows that PSO is feasible for mesh improvement.
Authors: S Gibson; C Fyock; E Grimson; T Kanade; R Kikinis; H Lauer; N McKenzie; A Mor; S Nakajima; H Ohkami; R Osborne; J Samosky; A Sawada Journal: Med Image Anal Date: 1998-06 Impact factor: 8.545
Authors: Leanne M Sutherland; Philippa F Middleton; Adrian Anthony; Jeffrey Hamdorf; Patrick Cregan; David Scott; Guy J Maddern Journal: Ann Surg Date: 2006-03 Impact factor: 12.969