| 研究生: |
賴郁欣 Lai, Yu-Xin |
|---|---|
| 論文名稱: |
磁振造影仿真大腦及病灶假體與其穿刺力學之研究 Research on MRI-and-Mechanical Mimicking Brain-and-Tumor Phantoms and Its Insertion Biomechanics |
| 指導教授: |
朱銘祥
Ju, Ming-Shaung |
| 學位類別: |
碩士 Master |
| 系所名稱: |
工學院 - 機械工程學系 Department of Mechanical Engineering |
| 論文出版年: | 2021 |
| 畢業學年度: | 109 |
| 語文別: | 中文 |
| 論文頁數: | 122 |
| 中文關鍵詞: | 幾何形狀仿真之大腦假體 、磁振造影可辨識假體 、類線性黏彈模型 、腦組織穿刺力學 |
| 外文關鍵詞: | geometry mimicking brain phantom, MRI distinguishable, quasilinear viscoelasticity, tissue insertion mechanics |
| 相關次數: | 點閱:232 下載:0 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
臨床上神經外科取樣手術過程中,腦組織會因開顱或手術器械穿刺使得腦脊髓液流失,導致目標點位置與術前規劃的位置有偏差,由於無法即時監測手術器械在腦組織的位置,進而導致定位失準,因此發展術中磁振導引即時監控腦組織與目標點,是目前神經外科手術重要研究之一。本研究欲發展幾何形狀仿真假體,其腦質假體與豬腦之機械性質相近,且能在磁振造影中使腫瘤假體與腦質假體呈現不同顯像,以應用於磁振造影相容手術機器人檢驗與醫師手術訓練之用。
首先由病患頭部磁振影像應用軟體3D Slicer、Solidworks與Meshmixer設計模具,以3D列印製作模具,並由半球形假體試件與豬腦進行鬆弛試驗,測試腦質假體與豬腦之黏彈性質,另外進行腫瘤假體標準試件準靜態試驗,測試其彈性性質,最後以臨床活檢針穿刺幾何形狀仿真假體與豬腦,測量穿刺過程力量之變化。藉由添加不同濃度的硫酸銅於腦質假體與腫瘤假體,改變其磁振造影的T1鬆弛時間。結果顯示0.6%洋菜濃度最適合作為腦質假體材料,由果凍蠟與0.4%洋菜濃度組合的腫瘤假體與膠質母細胞瘤彈性性質相近,組織內部的剛性為豬腦大於無腫瘤的幾何形狀仿真假體,但其差異對於人體感受甚小。將病患與幾何形狀仿真的假體的磁振造影像,以豪斯多夫距離與Dice係數比較發現兩者幾何相似度很高。總之,本研究發展的幾何形狀仿真假體,可能應用於磁振造影相容立體定位手術機器人檢驗以及神經外科手術術前規畫與教學。
During the clinical neurosurgery operation, patient’s brain will lose cerebrospinal fluid due to craniotomy or surgical instrument puncture, and the target may be displaced away from the pre-surgical planned position. Lacking information of intra-operative position of the needle tip in brain tissue will reduce the positioning accuracy. The development of intraoperative MRI method to monitor the needle and target points in real time is an important field of neurosurgery nowadays. The main goals of this study were two-fold, first, to develop geometry mimicking phantoms of human brain which can be used in neurosurgery. The mechanical properties imitate those of the patient’s brain, and the material is MRI visible and healthy tissue is distinguishable from the pathological tissue, so as to be applied to validate the accuracy and precision of the MRI compatible robot and surgeon training. Secondly, phantoms are characterized for application in validating the performance of a MRI-compatible surgical robot.
The MRI image of a patient from the literature were utilized to build the phantom. Software such as 3D Slicer, Solidworks and Meshmixer were employed to design the molds for the geometry mimicking phantom. The molds were fabricated by a 3D printer. Agar solution with concentration of 0.6% was chosen as the phantom cerebrum and 0.4% for the phantom tumor for glioblastoma. For MR visibility the phantom cerebrum and phantom tumor were doped with 3.2mM of copper sulfate and 1mM of copper sulfate respectively. Relaxation test of the phantom cerebrum material were performed on a material testing machine to characterize its viscoelasticity using the quasi-viscoelasticity theory. In addition, the quasi static compression tests of the phantom tumor were also conducted to characterize its elasticity. A clinical biopsy needle was utilized to perform insertion procedure on the brain-and-tumor phantom to characterize the biomechanics of the operation using the material testing machine and compared with the surgeon’s subjective feeling. T1 MRI of the phantom of the patient was acquired by a 3T-MR machine. The geometric accuracy of the phantom images was compared with those of the original images. The results show that the phantom material made of 0.6% agar has apparent Young’s modulus close to that of the swine brain. The phantom material made of 0.4% agar solution has Young’s modulus close to that of the swine brain glioblastoma.
Although the rigidity derived from the insertion force-displacement curve of the cerebrum phantom is greater than that of the swine, the surgeon detects no difference between the insertion forces of the human brain and the brain phantom. MR images analysis of the brain phantom of the patient shows that the jelly wax coated on tumor phantom surface can effectively block the diffusion of copper anion from cerebrum phantom. Hausdorff distance and Dice similarity analyses show the brain phantom has a high degree of similarity to original MR images and geometry of the patient brain when its MRI was acquired. In conclusion, a method for fabricating MRI and mechanical mimicking brain phantom of patient was developed and the insertion mechanics of the phantoms was evaluated. The phantoms may be applicable for preoperative planning, training of neurology surgeons and validating of MRI-guided surgical robots.
[1] J. S. Allen, H. Damasio, T. J. Grabowski, "Normal neuroanatomical variation in the human brain: an MRI-volumetric study, " American Journal of Physical Anthropology, vol. 118, no. 4, pp. 341-58, 2002.
[2] M. Peters, L. Jäncke, J.F. Staiger, G. Schlaug, Y. Huang, H. Steinmetz, "Unsolved Problems in Comparing Brain Sizes in Homo Sapiens, " Brain and Cognition, vol. 37, pp. 254-285, 1998.
[3] Eric Courchesne, Heather J. Chisum, Jeanne Townsend, Angilene Cowles, James Covington, Brian Egaas, Mark Harwood, Stuart Hinds, Gary A. Press, "Normal brain development and aging: quantitative analysis at in vivo MR imaging in healthy volunteers," Radiology, vol. 216, no. 3, pp. 672-682, 2000.
[4] K. Miller and K. Chinzei, "Constitutive Modelling of Brain Tissue: Experiment and Theory," Journal of Biomechanics, vol. 30, no. 11, pp. 1115-1121, 1997.
[5] K. Miller, "Constitutive Model of Brain Tissue Suitable for Finite Element Analysis of Surgical Procedures," Journal of Biomechanics, vol. 32, no. 5, pp. 531-537, 1999.
[6] K. Miller, K. Chinzei, G. Orssengo, and P. Bednarz, "Mechanical Properties of Brain Tissue in-vivo: Experiment and Computer Simulation," Journal of Biomechanics, vol. 33, no. 11, pp. 1369-1376, 2000.
[7] A. Gefen and S. S. Margulies, "Are in vivo and in situ Brain Tissues Mechanically Similar?," Journal of Biomechanics, vol. 37, no. 9, pp. 1339-1352, 2004.
[8] P. Aimedieu and R. Grebe, "Tensile Strength of Cranial Pia Mater: Preliminary Results," Journal of Neurosurgery, vol. 100, no. 1, pp. 111-114, 2004.
[9] X. Jin, K. H. Yang, and A. I. King, "Mechanical Properties of Bovine Pia–arachnoid Complex in Shear," Journal of Biomechanics, vol. 44, no. 3, pp. 467-474, 2011.
[10] X. Jin, J. B. Lee, L. Y. Leung, L. Zhang, K. H. Yang, and A. I. King, "Biomechanical Response of the Bovine Pia-arachnoid Complex to Tensile Loading at Varying Strain-rates," Stapp Car Crash Journal, vol. 50, pp. 637-649, 2006.
[11] X. Jin, C. Ma, L. Zhang, K. H. Yang, and A. I. King, "Biomechanical Response of the Bovine Pia-Arachnoid Complex to Normal Traction Loading at Varying Strain Rates," Stapp Car Crash Journal, vol. 51, pp. 115-126, 2007.
[12] X. Jin, H. Mao, K. H. Yang, and A. I. King, "Constitutive Modeling of Pia–Arachnoid Complex," Annals of Biomedical Engineering, vol. 42, no. 4, pp. 812-821, 2014.
[13] Gabriel Zada, Rose Du, and Edward R. Laws Jr.," Defining the “edge of the envelope”: patient selection in treating complex sellar-based neoplasms via transsphenoidal versus open craniotomy," Journal of Neurosurgery, vol. 114, pp. 286-300, 2011.
[14] G. Zada, P. Yashar, A. Robison, J. Winer, A. Khalessi, W. J. Mack, and S. L. Giannotta, "A proposed grading system for standardizing tumor consistency of intracranial meningiomas," Neurosurgical Focus, vol. 35, no. 6 , 2013.
[15] K. Itamura, K. Chang, J. Lucas, D. A. Donoho, S. Giannotta, and G. Zada, "Prospective clinical validation of a meningioma consistency grading scheme: association with surgical outcomes and extent of tumor resection," Journal of Neurosurgery, vol.131, pp.1356-1360, 2019.
[16] D. Chauvet, M. Imbault, L. Capelle, C. Demene, M. Mossad, C. Karachi, A. L. Boch, J. L. Gennisson, and M. Tanter, " In Vivo Measurement of Brain Tumor Elasticity Using Intraoperative Shear Wave Elastography," Ultraschall in der Medizin, vol. 37, pp.584-590, 2016.
[17] M. S. Shiroishi, S. Y. Cen, B. Tamrazi, F. D'Amore, A. Lerner, K. S. King, P. E. Kim, M. Law, D. H. Hwang, O. B. Boyko, and C. S. Liu, "Predicting Meningioma Consistency on Preoperative Neuroimaging Studies," Neurosurgery clinics of North America, vol. 27, pp. 145–154, 2016.3
[18] Lu Yiping, Xiong Ji, Geng Daoyinga, Yin Boa, "Prediction of the consistency of pituitary adenoma: A comparative study on diffusion-weighted imaging and pathological results," Journal of Neuroradiology, vol. 43, pp. 186-194, 2016.
[19] M. Simon, J. Guo, S. Papazoglou, H. Scholand-Engler, C. Erdmann, U. Melchert, M. Bonsanto, J. Braun, D. Petersen, "Non-invasive characterization of intracranial tumors by magnetic resonance elastography, " New Journal of Physics, vol. 15, 2013.
[20] M. Reiss-Zimmermann, K. J. Streitberger, I. Sack, J. Braun, F. Arlt, D. Fritzsch, and K. T. Hoffmann, "High Resolution Imaging of Viscoelastic Properties of Intracranial Tumours by Multi-Frequency Magnetic Resonance Elastography," Clinical neuroradiology, vol. 25, pp. 371-378, 2015.
[21] N. Sakai, Y. Takehara, S. Yamashita, N. Ohishi, H. Kawaji, T. Sameshima, S. Baba, H. Sakahara and H. Namba, "Shear Stiffness of 4 Common Intracranial Tumors Measured Using MR Elastography: Comparison with Intraoperative Consistency Grading," American Journal of Neuroradiology, vol. 37, pp. 1851-1859, 2016.
[22] L. Xu, Y. Lin, J. C. Han, Z. N. Xi, H. Shen, and P. Y. Gao, "Magnetic Resonance Elastography of Brain Tumors: Preliminary Results", Acta Radiologica, vol. 48, pp. 327-330, 2007.
[23] Daniel C. Stewart, Andrés Rubiano, Kyle Dyson, Chelsey S. Simmons, "Mechanical characterization of human brain tumors from patients and comparison to potential surgical phantoms," PloS one, vol. 12, p. e0177561, 2017.
[24] S. Pieper, M. Halle, and R. Kikinis, "3D Slicer," in 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821), vol. 1, Arlington, VA, USA, pp. 632-635, 2004.
[25] A. Fedorov, R. Beichel, J. Kalpathy-Cramer, J. Finet, J.-C. Fillion-Robin, S. Pujol, C. Bauer, D. Jennings, F. Fennessy, M. Sonka, J. Buatti, S. Aylward, J. V. Miller, S. Pieper, and R. Kikinis, "3D Slicer as an Image Computing Platform for the Quantitative Imaging Network," Magnetic Resonance Imaging, vol. 30, no. 9, pp. 1323-1341, 2012.
[26] J. R. Ryan, T. Chen, P. Nakaji, D. H. Frakes, and F. Gonzalez, "Ventriculostomy Simulation Using Patient-Specific Ventricular Anatomy, 3D Printing, and Hydrogel Casting," World Neurosurgery, vol. 84, no. 5, pp. 1333-1339, 2015.
[27] B. L. Tai, D. Rooney, F. Stephenson, P.-S. Liao, O. Sagher, A. J. Shih, and L. E. Savastano, "Development of a 3D-printed External Ventricular Drain Placement Simulator: Technical Note," vol. 123, no. 4, pp. 1070-1076, 2015.
[28] C. C. Ploch, C. S. S. A. Mansi, J. Jayamohan, and E. Kuhl, "Using 3D Printing to Create Personalized Brain Models for Neurosurgical Training and Preoperative Planning," World Neurosurgery, vol. 90, pp. 668-674, 2016.
[29] Hideo Kodama, "Automatic method for fabricating a three‐dimensional plastic model with photo‐hardening polymer," Review of Scientific Instruments, vol. 52, pp. 1770-1773, 1981.
[30] C. W. Hull, "Apparatus for production of three-dimensional objects by stereolithography", US Pat., 4575330, 1984.
[31] V. Horsley and R. H. Clarke, "The Structure and Functions of the Cerebellum Examined by a New Method," Brain, vol. 31, no. 1, pp. 45-124, 1908.
[32] E. A. Spiegel, H. T. Wycis, M. Marks, and A. J. Lee, "Stereotaxic Apparatus for Operations on the Human Brain," Science, vol. 106, no. 2754, pp. 349-350, 1947.
[33] G. N. Hounsfield, "Computerized Transverse Axial Scanning (Tomography): Part 1. Description of System," The British Journal of Radiology, vol. 46, no. 552, pp. 1016-1022, 1973.
[34] R. Damadian, "Tumor Detection by Nuclear Magnetic Resonance," Science, vol. 171, no. 3976, pp. 1151-1153, 1971.
[35] L. Leksell and B. Jernberg, "Stereotaxis and Tomography a Technical Note," Acta Neurochirurgica, vol. 52, no. 1, pp. 1-7, 1980.
[36] G. Eggers, J. Mühling, and R. Marmulla, "Image-to-patient Registration Techniques in Head Surgery," International Journal of Oral and Maxillofacial Surgery, vol. 35, no. 12, pp. 1081-1095, 2006.
[37] Y. Lu, C. Yeung, A. Radmanesh, R. Wiemann, P. M. Black, and A. J. Golby, "Comparative Effectiveness of Frame-based, Frameless and Intraoperative MRI Guided Brain Biopsy Techniques," World Neurosurgery, vol. 83, no. 3, pp. 261-268, 2015.
[38] H. M. Shao, J. Y. Chen, T. K. Truong, I. S. Reed, and Y. S. Kwoh, "A New CT-Aided Robotic Stereotaxis System," Proc Annu Symp Comput Appl Med Care, pp. 668-672, 1985.
[39] D. Glauser, H. Fankhauser, M. Epitaux, J.-L. Hefti, and A. Jaccottet, "Neurosurgical Robot Minerva: First Results and Current Developments," Journal of Image Guided Surgery, vol. 1, no. 5, pp. 266-272, 1995.
[40] A. L. Goldson, "Preliminary Clinical Experience with Intraoperative Radiotherapy," J Natl Med Assoc, vol. 70, no. 7, pp. 493-495, 1978.
[41] M. Heinig, U.G.Hofmann, A. Schlaefer, "Calibration of the motor-assisted robotic stereotaxy system: MARS," International journal of computer assisted radiology and surgery, vol. 7, pp. 911-920, 2012.
[42] G. R. Sutherland, P. B. McBeth, and D. F. Louw, "NeuroArm: an MR Compatible Robot for Microsurgery," International Congress Series, vol. 1256, pp. 504-508, 2003.
[43] A. E. Sloan, M. S. Ahluwalia, J. Valerio-Pascua, S. Manjila, M. G. Torchia, S. E. Jones, J. L. Sunshine, M. Phillips, M. A. Griswold, M. Clampitt, C. Brewer, J. Jochum, M. V. McGraw, D. Diorio, G. Ditz, and G. H. Barnett, "Results of the NeuroBlate System First-in-humans Phase I Clinical Trial for Recurrent Glioblastoma," Journal of Neurosurgery, vol. 118, no. 6, pp. 1202-1219, 2013.
[44] P. A. Starr, A. J. Martin, J. L. Ostrem, P. Talke, N. Levesque, and P. S. Larson, "Subthalamic Nucleus Deep Brain Stimulator Placement Using High-field Interventional Magnetic Resonance Imaging and a Skull-mounted Aiming Device: Technique and Application Accuracy," Journal of Neurosurgery, vol. 112, no. 3, pp. 479-490, 2010.
[45] P. A. Starr, L. C. Markun, P. S. Larson, M. M. Volz, A. J. Martin, and J. L. Ostrem, "Interventional MRI–guided Deep Brain Stimulation in Pediatric Dystonia: First Experience with the ClearPoint System," Journal of Neurosurgery, vol. 14, no. 4, pp. 400-408, 2014.
[46] 何炳林, "磁振造影相容之神經外科用立體定位手術機器人之發展," 碩士論文, 國立成功大學機械工程學系, 臺灣臺南市, 2016.
[47] 陳煌霖, "磁振造影相容之神經外科手術用五軸立體定位機器系統研究," 碩士論文, 國立成功大學機械工程學系, 臺灣臺南市, 2018.
[48] 彭郁濃, "術中核磁共振影像導引立體定位手術機器人," 碩士論文, 國立成功大學機械工程學系, 臺灣臺南市, 2019.
[49] E. L. Madsen and G. D. Fullerton, "Prospective Tissue-mimicking Materials for Use in NMR Imaging Phantoms," Magnetic Resonance Imaging, vol. 1, no. 3, pp. 135-141, 1982.
[50] I. Mano, H. Goshima, M. Nambu, and M. Iio, "New Polyvinyl Alcohol Gel Material for MRI Phantoms," Magnetic Resonance in Medicine, vol. 3, no. 6, pp. 921-926, 1986.
[51] P. Brzozowski, K. I. Penev, F. M. Martinez, T. J. Scholl, and K. Mequanint, "Gellan Gum-based Gels with Tunable Relaxation Properties for MRI Phantoms," Magnetic Resonance Imaging, vol. 57, pp. 40-49, 2019.
[52] A. Hellerbach, V. Schuster, A. Jansen, and J. Sommer, "MRI Phantoms - Are There Alternatives to Agar?," PloS one, vol. 8, no. 8, p. e70343, 2013.
[53] M. D. Mitchell, H. L. Kundel, L. Axel, and P. M. Joseph, "Agarose as a Tissue Equivalent Phantom Material for NMR Imaging," Magnetic Resonance Imaging, vol. 4, no. 3, pp. 263-266, 1986.
[54] K. A. Kraft, P. P. Fatouros, G. D. Clarke, and P. R. S. Kishore, "An MRI Phantom Material for Quantitative Relaxometry," Magnetic Resonance in Medicine, vol. 5, no. 6, pp. 555-562, 1987.
[55] F. A. Howe, "Relaxation Times in Paramagnetically Doped Agarose Gels as a Function of Temperature and Ion Concentration," Magnetic Resonance Imaging, vol. 6, no. 3, pp. 263-270, 1988.
[56] J. C. Blechinger, E. L. Madsen, and G. R. Frank, "Tissue-mimicking Gelatin–agar Gels for Use in Magnetic Resonance Imaging Phantoms," Medical Physics, vol. 15, no. 4, pp. 629-636, 1988.
[57] M. Bucciolini, L. Ciraolo, and B. Lehmann, "Simulation of Biologic Tissues by Using Agar Gels at Magnetic Resonance Imaging," Acta Radiologica, vol. 30, no. 6, pp. 667-669, 1989.
[58] J. O. Christoffersson, L. E. Olsson, and S. Sjöberg, "Nickel-Doped Agarose Gel Phantoms in MR Imaging," Acta Radiologica, vol. 32, no. 5, pp. 426-431, 1991.
[59] 蕭正豪, "機械性質與磁振造影仿真之大腦假體研究," 碩士論文, 國立成功大學機械工程學系, 臺灣臺南市, 2020.
[60] A. M. Okamura, C. Simone and M. D. O'Leary, "Force modeling for needle insertion into soft tissue," in IEEE Transactions on Biomedical Engineering, vol. 51, no. 10, pp. 1707-1716, 2004.
[61] S. P. DiMaio and S. E. Salcudean, "Needle insertion modeling and simulation," in IEEE Transactions on Robotics and Automation, vol. 19, no. 5, pp. 864-875, 2003.
[62] M. G. Jushiddi, J. Mulvihill, D. Chovan, A. Mani, C. Shanahan, C. Silien, S. A. Md Tofail, and P. Tiernan, "Simulation of biopsy bevel-tipped needle insertion into soft-gel," Computers in biology and medicine, vol. 111, 2019.
[63] L. Murong, L. Yong, G. Dedong, H. Yingda, Z. Xiong, "A novel material point method (MPM) based needle-tissue interaction model," Computer Methods in Biomechanics and Biomedical Engineering, 2021.
[64] S. Jiang, P. Li, Y. Yu, J. Liu, Z. Yang, "Experimental study of needle–tissue interaction forces: Effect of needle geometries, insertion methods and tissue characteristics," Journal of Biomechanics, vol. 47, pp.3344-3353, 2014.
[65] F. Casanova, P.R. Carney, M. Sarntinoranont, "In vivo evaluation of needle force and friction stress during insertion at varying insertion speed into the brain," Journal of Neuroscience Methods, vol. 237, pp. 79-89, 2014.
[66] N. H. Hosseini, R. Hoffmann, S. Kisban, T. Stieglitz, O. Paul and P. Ruther, "Comparative study on the insertion behavior of cerebral microprobes," 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4711-4714, 2007.
[67] Y. C. Fung, Biomechanics: Mechanical Properties of Living Tissues, 2nd ed. Springer, New York, 1993.
[68] D. P. Huttenlocher, G. A. Klanderman and W. J. Rucklidge, "Comparing images using the Hausdorff distance," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 9, pp. 850-863, 1993.
[69] L. R. Dice, "Measures of the amount of ecologic association between species," Ecology, vol. 26, no. 3, pp. 297-302, 1945.
[70] J. Egger, T. Kapur, A. Fedorov, S. Pieper, J. V Miller, H. Veeraraghavan, et al., "GBM volumetry using the 3D Slicer medical image computing platform," Scientific reports, vol. 3, pp. 1364, 2013.
[71] A. Altermatt, F. Santini, X. Deligianni, S. Magon, T. Sprenger, L. Kappos, P. Cattin, J. Wuerfel, and L. Gaetano, "Design and Construction of an Innovative Brain Phantom Prototype for MRI," Magnetic Resonance in Medicine, vol. 81, no. 2, pp. 1165-1171, 2019.