18 March 2016 Surface mesh to voxel data registration for patient-specific anatomical modeling
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Abstract
Virtual Physiological Human (VPH) models are frequently used for training, planning, and performing medical procedures. The Regional Anaesthesia Simulator and Assistant (RASimAs) project has the goal of increasing the application and effectiveness of regional anesthesia (RA) by combining a simulator of ultrasound-guided and electrical nerve-stimulated RA procedures and a subject-specific assistance system through an integration of image processing, physiological models, subject-specific data, and virtual reality. Individualized models enrich the virtual training tools for learning and improving regional anaesthesia (RA) skills. Therefore, we suggest patient-specific VPH models that are composed by registering the general mesh-based models with patient voxel data-based recordings. Specifically, the pelvis region has been focused for the support of the femoral nerve block. The processing pipeline is composed of different freely available toolboxes such as MatLab, the open Simulation framework (SOFA), and MeshLab. The approach of Gilles is applied for mesh-to-voxel registration. Personalized VPH models include anatomical as well as mechanical properties of the tissues. Two commercial VPH models (Zygote and Anatomium) were used together with 34 MRI data sets. Results are presented for the skin surface and pelvic bones. Future work will extend the registration procedure to cope with all model tissue (i.e., skin, muscle, bone, vessel, nerve, fascia) in a one-step procedure and extrapolating the personalized models to body regions actually being out of the captured field of view.
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Júlia E. E. de Oliveira, Júlia E. E. de Oliveira, Paul Giessler, Paul Giessler, András Keszei, András Keszei, Andreas Herrler, Andreas Herrler, Thomas M. Deserno, Thomas M. Deserno, } "Surface mesh to voxel data registration for patient-specific anatomical modeling", Proc. SPIE 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, 978625 (18 March 2016); doi: 10.1117/12.2217491; https://doi.org/10.1117/12.2217491
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