J Shanghai Jiaotong Univ Sci ›› 2021, Vol. 26 ›› Issue (1): 33-39.doi: 10.1007/s12204-020-2232-x
Finite Element Modeling of Human Thorax Based on MRI Images for EIT Image Reconstruction
HUANG Ningning (黄宁宁), MA Yixin (马艺馨), ZHANG Mingzhu (张明珠), GE Hao (葛浩), WU Huawei (吴华伟)
- (1. Department of Instrument Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;
2. Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai 200240, China;
3. Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China)
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出版日期:2021-02-28发布日期:2021-01-19 -
通讯作者:MA Yixin (马艺馨) E-mail:y.ma@sjtu.edu.cn
Finite Element Modeling of Human Thorax Based on MRI Images for EIT Image Reconstruction
HUANG Ningning (黄宁宁), MA Yixin (马艺馨), ZHANG Mingzhu (张明珠), GE Hao (葛浩), WU Huawei (吴华伟)
- (1. Department of Instrument Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;
2. Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai 200240, China;
3. Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China)
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Online:2021-02-28Published:2021-01-19 -
Contact:MA Yixin (马艺馨) E-mail:y.ma@sjtu.edu.cn
摘要： Electrical impedance tomography (EIT) image reconstruction is a non-linear problem. In general, finite element model is the critical basis of EIT image reconstruction. A 3D human thorax modeling method for EIT image reconstruction is proposed herein to improve the accuracy and reduce the complexity of existing finite element modeling methods. The contours of human thorax and lungs are extracted from the layers of magnetic resonance imaging (MRI) images by an optimized Otsu’s method for the construction of the 3D human thorax model including the lung models. Furthermore, the GMSH tool is used for finite element subdivision to generate the 3D finite element model of human thorax. The proposed modeling method is fast and accurate, and it is universal for different types of MRI images. The effectiveness of the proposed method is validated by extensive numerical simulation in MATLAB. The results show that the individually oriented 3D finite element model can improve the reconstruction quality of the EIT images more effectively than the cylindrical model, the 2.5D model and other human chest models.
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HUANG Ningning (黄宁宁), MA Yixin (马艺馨), ZHANG Mingzhu (张明珠), GE Hao (葛浩), WU Huawei (吴华伟). Finite Element Modeling of Human Thorax Based on MRI Images for EIT Image Reconstruction[J]. J Shanghai Jiaotong Univ Sci, 2021, 26(1): 33-39.
HUANG Ningning (黄宁宁), MA Yixin (马艺馨), ZHANG Mingzhu (张明珠), GE Hao (葛浩), WU Huawei (吴华伟). Finite Element Modeling of Human Thorax Based on MRI Images for EIT Image Reconstruction[J]. J Shanghai Jiaotong Univ Sci, 2021, 26(1): 33-39.
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| [1] | EY¨UBOGLU B M, BROWN B H, BARBER D C.Problems of cardiac output determination from electricalimpedance tomography scans [J]. Clinical Physicsand Physiological Measurement, 1988, 9(Sup. A): 71-77. |
| [2] | WANG Y H, PAN Y, YANG M. Application of electricalimpedance tomography for postoperative lungrecruitment in patients undergoing off pump coronaryartery bypass grafting surgery [J]. Journal of ShanghaiJiao Tong University (Medical Science), 2018, 38(6):653-657 (in Chinese). |
| [3] | SHI X T, YOU F S, FU F, et al. Sensitivity research ondetection of impedance distortion inside human headphantom by electrical impedance tomography method[J]. Chinese Medical Equipment Journal, 2011, 32(5):1-3 (in Chinese). |
| [4] | XU K, LIU M H, LIU D, et al. Simulation of multiphaseflows in microscale hemodialysis [J]. ChineseJournal of Computational Physics, 2009, 26(1): 49-56(in Chinese). |
| [5] | CAO Z, WANG H X. Electromagnetic model and imagereconstruction algorithms based on EIT system [J].Transactions of Tianjin University, 2006, 12(6): 420-424. |
| [6] | LIU D, KHAMBAMPATI A K, DU J F. A parametriclevel set method for electrical impedance tomography[J]. IEEE Transactions on Medical Imaging, 2018,37(2): 451-460. |
| [7] | GRYCHTOL B, ADLER A. Uniform background assumptionproduces misleading lung EIT images [J].Physiological Measurement, 2013, 34(6): 579-593. |
| [8] | GONZ′ALEZ G, HUTTUNEN J M J,KOLEHMAINEN V, et al. Experimental evaluationof 3D electrical impedance tomography with totalvariation prior [J]. Inverse Problems in Science andEngineering, 2016, 24(8): 1411-1431. |
| [9] | SUN B Y, YUE S H, HAO Z H, et al. An improvedTikhonov regularization method for lung cancermonitoring using electrical impedance tomography[J]. IEEE Sensors Journal, 2019, 19(8): 3049-3057. |
| [10] | ZHANG S, XU G Z, ZHANG X Y, et al. Computationof a 3-D model for lung imaging with electricalimpedance tomography [J]. IEEE Transactions onMagnetics, 2012, 48(2): 651-654. |
| [11] | CRABB M G, DAVIDSON J L, LITTLE R, et al. Mutualinformation as a measure of image quality for 3Ddynamic lung imaging with EIT [J]. Physiological Measurement,2014, 35(5): 863-879. |
| [12] | FAN W R, WANG H X. 3D modelling of the humanthorax for ventilation distribution measured throughelectrical impedance tomography [J]. MeasurementScience and Technology, 2010, 21(11): 115801. |
| [13] | HUANG Z, CHEN Z. Three-dimensional finite elementmodeling of a maxillary premolar tooth based on themicro-CT scanning: A detailed description [J]. Journalof Huazhong University of Science and Technology(Medical Sciences), 2013, 33(5): 775-779. |
| [14] | CHEN X Y, CHU M L, CHANG X M, et al. 3D EIT model construction based on lung and image reconstructionresearch [J]. Chinese Journal of BiomedicalEngineering, 2017, 36(5): 622-626 (in Chinese). |
| [15] | OTSU N. A threshold selection method from gray-levelhistograms [J]. IEEE Transactions on Systems, Man,and Cybernetics, 1979, 9(1): 62-66. |
| [16] | SUN H Z, MA Y X, WU H W, et al. An improvedOTSU’s method for CT image boundary contour extraction[C]//IEEE International Conference on ImagingSystems and Techniques. Chania, Greece: IEEE,2016: 493-497. |
| [17] | LI L, MA Y X, WU H W. Lung boundary detectionmethod based on binary morphology theory [J]. ChineseJournal of Medical Instrumentation, 2010, 34(6):413-417 (in Chinese). |
| [18] | ZHANG F. 3D solid reconstruction based on tomographicimage [D]. Hefei, China: University of Scienceand Technology of China, 2009 (in Chinese). |
| [19] | WANG H X, HUANG W R, FAN W R. Measurementpattern for 3D electrical impedance tomography [J].Journal of Tianjin University, 2012, 45(8): 729-734(in Chinese). |
| [20] | ZHANG M, YUE S H. Application of a novel optimization method for EIT imaging [J]. Transducer andMicrosystem Technologies, 2018, 37(8): 154-156 (in Chinese). |
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