초탄성 구성 모델 기반 인체 폐의 호흡 시뮬레이션 및 이의 임상 검증

국문 초록 (Abstract)

본 연구에서는 초탄성 구성 모델을 기반으로 인체 폐의 호흡 시뮬레이션을 수행하고 그 결과를 임상 데이터와 비교하였다. 즉, 초탄성 재료 특성을 보이는 인체 폐의 재료 거동을 모사하기 위해 다양한 초탄성 구성 모델, 이를테면 Mooney Rivilin, Ogden, Van der Waals, neo-Hooke, Yeoh, polynomial, reduced polynomial 모델을 바탕으로, 문헌상으로 보고된 사체 폐의 이축 인장-압축 선도를 가장 잘 모사하는 구성 모델을 채택, 재료 파라미터를 결정하였다. 한편, 3명의 실험자의 흡기 상태 폐 CT 이미지로부터 유한요소 모델을 구축한 후, 호기 상태를 구현하였으며, 시간에 따른 변위장(magnitude and resultant)의 변화를 평가하였다. 나아가, 제안된 기법의 검증을 위해 시리즈 시뮬레이션 결과를 3명의 실험자의 호기 상태 폐 CT 이미지와 정량적으로 비교하였으며, 폐 상층 횡단면적(superior-inferior cross sectional area)의 상대오차는 0.2~7.4% 범위 내에서 잘 일치하는 것을 확인하였다.

다국어 초록 (Multilingual Abstract)

In this study, we performed respiratory simulation of human body lung based on hyperelastic constitutive model and compared the results with clinical data. That is, in order to simulate the material behavior of human lung that exhibits hyperelastic material properties, based on various hyperelastic constitutive models, for example, Mooney Rivilin, Ogden, Van der Waals, neo-Hooke, Yeoh, polynomial, reduced polynomial models. Based on this, material parameters were determined by adopting a constitutive model that best simulates the biaxial tension-compression diagram leads of the body lung reported in the literature. On the other hand, after constructing a finite element model from the inhalation lung CT of three experimenters, the exhalation state was implemented and the change in displacement and resultant with time was evaluated. Furthermore, for verification of the provided method, simulation results of the series were quantitatively compared with the exhaled state and lung CT images of three experimenters, and confirmed to be well matched within the range of 0.2 to 7.4% relative error of the lung superior-inferior cross sectional area.

목차 (Table of Contents)

• 초록
• Abstract
• 1. 서론
• 2. 본론
• 3. 결과
• 4. 결론
• 참고문헌(References)

참고문헌 (Reference)

1 송용섭, "폐 반고형결절: 개요와 관리 가이드라인" 대한영상의학회 78 (78): 309-320, 2018

2 Krimsky, W. S., "Thoracoscopic Detection of Occult Indeterminate Pulmonary Nodules Using Bronchoscopic Pleural Dye Marking" 4 (4): 1-, 2014

3 Zhang, T., "Technical Note: A Novel Boundary Condition Using Contact Elements for Finite Element Deformable Image Registration" 31 (31): 2412-2415, 2004

4 Villard, P, "Simulation of Lung Behaviour with Finite Elements: Influence of Bio-Mechanical Parameters" Third International Conference on Medical Information Visualization -BioMedical Visualization 2005

5 Tehrani, J. N., "Sensitivity of Tumor Motion Simulation Accuracy to Lung Biomechanical Modeling Approaches and Parameters" 60 (60): 8833-8849, 2015

6 Ji Won Lee, "Planting Seeds into the Lung: Image-Guided Percutaneous Localization to Guide Minimally Invasive Thoracic Surgery" 대한영상의학회 20 (20): 1498-1514, 2019

7 Werner, R., "Patient-specific Finite Element Modeling of Respiratory Lung Motion Using 4D CT Image Data" 36 (36): 1500-1511, 2009

8 Eom, J., "Modeling Respiratory Motion for Cancer Radiation Therapy based on Patient-specific 4DCT Data" 12 (12): 348-355, 2009

9 Shirzadi, Z., "Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling" 2012

10 DeCarlo, D., "Integrating Anatomy and Physiology for Behavior Modeling" 3 : 19-22, 1955

11 Kuo, S. -W., "Electromagnetic Navigation Bronchoscopy Localization versus Percutaneous CT-guided Localization for Lung Resection via Video-assisted Thoracoscopic Surgery: A Propensity-matched Study" 8 (8): 2019

12 Al-Mayah, A., "Contact Surface and Material Nonlinearity Modeling of Human Lungs" 53 (53): 305-317, 2008

13 Chen, Y. -R., "CT-guided Hook Wire Localization of Subpleural Lung Lesions for Video-assisted Thoracoscopic Surgery (VATS)" 78 (78): 911-918, 2007

14 Ehrhardt, J., "An Optical Flow based Method for Improved Reconstruction of 4-D CT Data Sets Acquired during Free Breathing" 34 (34): 711-721, 2007

15 Dawson, L., "Advances in Image-guided Radiation Therapy" 25 (25): 938-946, 2007

16 Vedam, S., "Acquiring a Four-Dimensional Computed Tomography Dataset Using an External Respiratory Signal" 48 (48): 45-62, 2003

17 Berger, L., "A Poroelastic Model Coupled to a Fluid Network with Applications in Lung Modeling" 32 (32): 1-17, 2016

18 McClelland, J., "A Continuous 4-D Motion Model from Multiple Respiratory Cycles for Use in Lung Radiotherapy" 33 (33): 3348-3358, 2006