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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/25621完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳永耀 | |
| dc.contributor.author | Jian-Wen Liu | en |
| dc.contributor.author | 劉建玟 | zh_TW |
| dc.date.accessioned | 2021-06-08T06:21:39Z | - |
| dc.date.copyright | 2006-08-04 | |
| dc.date.issued | 2006 | |
| dc.date.submitted | 2006-07-31 | |
| dc.identifier.citation | W. C. Dewey, L. E. Hopwood, S.A. Sapareto, L.E. Gerweck: Cellular response to combination of hyperthermia and radistion. Radiology. 1977 Vol.123, pp.464-477
Isaksson M, Jalden J, Murphy MJ.: On using an adaptive neural network to predict lung tumor motion during respiration for radiotherapy applications. Med Phys. 2005 Dec;32(12):3801-9. Ebbini E: Multiple-Focus Ultrasound Phased-Array Pattern Synthesis – Optimal Driving-Signal Distributions for Hyperthermia. IEEE T ULTRASON FERR 1989, 36(5):540-548 Ebbini E, CAIN C: Experimental Evaluation of A Prototype Cylindrical Section Ultrasound Hyperthermia Phased-Array Applicator. IEEE T ULTRASON FERR 1991, 38(5):510-520 Nyborg W L: Heat generation by ultrasound in a relaxing medium. J. Acoust. Soc. Amer. 1981 310-2 Daum D, Hynynen K: A 256-element ultrasonic phased array system for the treatment of large volumes of deep seated tissue. IEEE T ULTRASON FERR 1999, 46(5):1254-1268. Pennes, H. H.: Analysis of tissue and arterial blood temperature in the resting human forearm. J. Aool. Phys. 1, 1948, 93-122 Sapareto S A and Dewey W C: Thermal dose determination in cancer therapy. Int. J. Radiat. Oncol. Biol. Phys., 10, 1984, 787-800 Damianou C A, Hynynen K and Fan X: Evaluation of accuracy of a theoretical model for predicting the necrosed tissue volume during focused ultrasound surgery. IEEE Trans. Ultrason. Ferroelect. Freq. Contr., 42, 1995, 182-187 Canny, John: A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-8, No. 6, 1986, pp. 679-698. Wu H, Sharp GC, Salzberg B, Kaeli D, Shirato H, Jiang SB: A finite state model for respiratory motion analysis in image guided radiation therapy. Phys Med Biol 2004, 49(23):5357-5372. Tokuda J, Morikawa S, Dohi T, Hata N: Motion tracking in MR-guided liver therapy by using navigator echoes and projection profile matching. Acad Radiol 2004, 11(1):111-120. Sharp GC, Jiang SB, Shimizu S, Shirato H: Prediction of respiratory tumour motion for real-time image-guided radiotherapy. Phys Med Biol 2004, 49(3):425-440. Pernot M, Tanter M, Fink M: 3-D real-time motion correction in high-intensity focused ultrasound therapy. ULTRASOUND IN MEDICINE AND BIOLOGY 2004, 30(9):1239-1249. Liu HL, Chen YY, Yen JY, Lin WL: Treatment time reduction for large thermal lesions by using a multiple 1D ultrasound phased array system. Phys Med Biol 2003, 48(9):1173-1190. Kennedy JE, Wu F, ter Haar GR, Gleeson FV, Phillips RR, Middleton MR, Cranston D: High-intensity focused ultrasound for the treatment of liver tumours. Ultrasonics 2004, 42(1-9):931-935. Kakar M, Nystrom H, Aarup LR, Nottrup TJ, Olsen DR.: Respiratory motion prediction by using the adaptive neuro fuzzy inference system (ANFIS). Phys Med Biol. 2005 Oct 7;50(19):4721-8. Epub 2005 Sep 21. Gregory C Sharp, Steve B Jiang, Shinichi Shimizu and Hiroki Shirato: Prediction of respiratory tumour motion for real-time image-guided radiotherapy. Phys. Med. Biol. 49 No 3 (7 February 2004) 425-440 Alexander F Kolen, Naomi R Miller, Eltayeb E Ahmed and Jeffrey C Bamber: Characterization of cardiovascular liver motion for the eventual application of elasticity imaging to the liver in vivo. Phys. Med. Biol. 49 No 18 (21 September 2004) 4187-4206 Canny Edge Detection Tutorial. http://www.pages.drexel.edu/~weg22/can_tut.html Backpropagation http://neuron.csie.ntust.edu.tw/homework/90/NN/90homework#1/M8907114/neural.htm | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/25621 | - |
| dc.description.abstract | 高能聚焦超音波是一種非侵入式治療腫瘤的新方法，主要是利用超音波在腫瘤部位聚焦提高溫度燒灼惡性細胞以達到治療的目的。其中我們可經由電子式的聚焦藉由調整相位陣列換能器的相位與振幅來改變聚焦點的位置，且由於聚焦超音波所形成的焦點相當小，因此可以精確地加熱於腫瘤部位。
當利用聚焦超音波做治療時，我們必須準確地將焦點聚焦在所要治療的患部以避免對正常的組織造成傷害。由於人體內一些內臟器官，如肝臟、肺臟，它們會因呼吸而受橫膈膜的擠壓在一個三度空間中運動，此一運動會造成聚焦超音波無法準確持續聚焦於患部上而造成治療效率的降低，更嚴重甚至會傷害到正常的組織造成不可預期的後果。 在本論文中，我們利用伺服馬達帶動目標物作非規則往復運動模擬內臟因呼吸所形成的運動軌跡，再以CCD攝影機模仿MRI抓取影像進而得知目標物的所在位置，為了克服在影像處理系統方面處理影像所造成的延遲，我們採用類神經網路來預測目標物的下一個和下下一個取樣時間點位置。追蹤方面以雷射筆的光點替代聚焦點追蹤目標物，加熱方面利用內插重建取樣點之間的軌跡，達到持續追蹤加熱的目標。如此預測演算法可以克服系統延遲的問題，內插重建軌跡能減少聚焦點與目標物之間距離的誤差，使得超音波能更精確的聚焦在患部上。 | zh_TW |
| dc.description.abstract | High intensity focus ultrasound is a non-invasive method to treat the tumor. A phased array transducer has the ability to change focus position or produce multi-focus by adjusting the amplitude and phase of each element on the surface of transducer. The characteristic of phased array can be utilized to kill the tumor that has reciprocation due to breathing within human body.
Image guidance in hyperthermia offers the potential for deliver thermal dosage to a moving tumor precisely. Because MRI image provides not only the temperature varying but also other information, combining MRI with HIFU for treating tumor is the main purpose. In this study, CCD image is used to imitate MRI image. A platform is controlled by servo-motor to simulate the organ motion due to breathing. However, tracking a target via image processing takes a small period of time including of image acquisition, image processing and delay of communications. To overcome the whole system time delay, it is necessary to develop a predictive algorithm. Neural network is used to predict the future trend of tumor to overcome the system time delay. And interpolation is used to reconstruct trajectory between sampling points to let the heating can be continuous. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T06:21:39Z (GMT). No. of bitstreams: 1 ntu-95-R93921072-1.pdf: 2552444 bytes, checksum: cc66175b626fd4819e845a50cc491268 (MD5) Previous issue date: 2006 | en |
| dc.description.tableofcontents | 摘要 II
Abstract III Contents IV List of Figures VII List of Tables XI List of Tables XI Chapter 1 Introduction 1 1.1 Introduction 1 1.2 Motivation and problem formulation 6 1.3 Purpose and the approach of this thesis 7 1.4 Organization 8 Chapter 2 Mathematical Model 10 2.1 Introduction 10 2.2 Calculations of acoustic field 11 2.3 Focusing technique 13 2.4 Temperature calculations 16 2.5 Thermal dose calculations 17 2.6 Summary 18 Chapter 3 Image Processing and Servo Motor Control 19 3.1 Introduction 19 3.2 Canny edge detection 20 3.2.1 Noise filtering 22 3.2.2 Finding the edge strength 23 3.2.3 Edge direction traced 24 3.2.4 Non-maximum suppression 25 3.2.5 Hysteresis threshold 25 3.3 Two-dimension cross correlation 26 3.4 DC servo motor control 28 3.4.1 Correction of position error accumulation 32 3.5 Summary 34 Chapter 4 Neural Network Predictive Algorithm for Liver Tumors with Motion Prediction and Tracking Control 35 4.1 Introduction 35 4.2 Neural network predictive algorithm 36 4.2.1 Basic structure of neural network 37 4.2.2 Backpropagation 38 4.3 Neural network structure in this study 41 4.3.1 Structure used 41 4.3.2 Sampling rate determination 43 4.3.3 Input normalize 46 4.3.3 Process of simulation 46 4.4 Simulation results of pre-processing effect 49 4.4.1 Normalize factor 49 4.4.2 Result of prediction 53 4.5 Summary 56 Chapter 5 Results of Simulation and Experiment 57 5.1 Simulation of organ motion prediction 57 5.2 Flowchart of experiment 65 5.3 Tracking experiment 70 5.4 Heating simulation 74 5.4.1 Fixed heating 75 5.4.2 Tracking heating 77 5.5 Heating experiment 83 5.5 Summary 89 Chapter 6 Discussion and Conclusion 90 Reference 93 | |
| dc.language.iso | en | |
| dc.subject | tumor motion | en |
| dc.subject | HIFU | en |
| dc.subject | neural network | en |
| dc.title | 以運動軌跡預測及追蹤控制方法進行肝腫瘤高能超音波熱治療 | zh_TW |
| dc.title | High Intensity Focused Ultrasound Thermal Therapy for Liver Tumors with Motion Prediction and Tracking Control | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 94-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 林文澧 | |
| dc.contributor.oralexamcommittee | 嚴家鈺,劉浩澧 | |
| dc.subject.keyword | 高能聚焦超音波,類神經網路,腫瘤運動, | zh_TW |
| dc.subject.keyword | HIFU,neural network,tumor motion, | en |
| dc.relation.page | 96 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2006-08-01 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
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