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안범모,박기한,이효상,김정,An, Beom-Mo,Park, Gi-Han,Lee, Hyo-Sang,Kim, Jeong 제어로봇시스템학회 2010 제어로봇시스템학회지 Vol.16 No.3
본 논문에서는 지금까지 이루어진 전립선 암 진단 및 치료를 위한 로봇기술 적용 사례 관련 연구들을 조사하여 체계적인 분류 및 분석 작업을 수행함으로써 현재 기술 동향을 파악하고, 앞으로 나아갈 연구 방향을 제시하고자 한다.
Efficient Soft Tissue Characterization under Large Deformations in Medical Simulations
안범모,김정 한국정밀공학회 2009 International Journal of Precision Engineering and Vol. No.
The modeling of soft tissue behavior is essential for virtual reality (VR)-based medical simulation, providing a safe and objective medium for training of the medical personnel. This paper presents a soft tissue modeling framework including instrumentation design, in vitro organ experiments and material property characterization. As observed from the force responses measured by a force transducer, the tissue was assumed as a nonlinear, continuous, incompressible, homogeneous and isotropic material for modeling. An electromechanical indentation system to measure the mechanical behavior of soft tissues was designed, and a series harvested organ in vitro experiments were performed. The non-linear soft tissue model parameters were then extracted by matching finite element model predictions with the empirical data. The soft tissue characterization algorithm could become computationally efficient by reducing the number of parameters. The developed tissue models are suitable for computing accurate reaction forces on surgical instruments and for computing deformations of organ surfaces for the VR based medical simulation.
유한요소 모델 변수의 역 추정법을 이용한 생체의 물성 규명
안범모(Bummo Ahn),김영진(Yeongjin Kim),신현정(Jennifer H. Shin),김정(Jung Kim) 대한기계학회 2009 大韓機械學會論文集A Vol.33 No.11
An inverse finite element (FE) model parameter estimation algorithm can be used to characterize mechanical properties of biological tissues. Using this algorithm, we can consider the influence of material nonlinearity, contact mechanics, complex boundary conditions, and geometrical constraints in the modeling. In this study, biomechanical experiments on macro and micro samples are conducted and characterized with the developed algorithm. Macro scale experiments were performed to measure the force response of porcine livers against mechanical loadings using one-dimensional indentation device. The force response of the human liver cancer cells was also measured by the atomic force microscope (AFM). The mechanical behavior of porcine livers (macro) and human liver cancer cells (micro) were characterized with the algorithm via hyperelastic and linear viscoelastic models. The developed models are suitable for computing accurate reaction force on tools and deformation of biomechanical tissues.