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題名 | 以影像自動化調整錐狀射束電腦斷層的窗寬和窗位的研究=Auto Adjust Window and Level on CBCT (Cone Beam Computed Tomography) Image |
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作者 | 李奇勇; 包舜華; 洪文欣; 劉千如; 呂志得; Lee, Chi-yung; Pao, Sun-hua; Hung, Wen-hsin; Liu, Chien-ru; Leu, Jyh-der; |
期刊 | 放射治療與腫瘤學 |
出版日期 | 20140300 |
卷期 | 21:1 2014.03[民103.03] |
頁次 | 頁55-61 |
分類號 | 416.14 |
語文 | chi |
關鍵詞 | 迭代式濾波器; 電腦斷層; 對比度; Iterative filter; CT; Contrast; |
中文摘要 | 目的:在比較手動調整和自動化調整電腦斷層影像的窗寬和窗位(Window and Level, WL)對錐狀射束電腦斷層(Cone Beam Computed Tomography, CBCT)影像中材質的辨識度的差異。材料與方法:使用設備為Elekta Synergy直線加速器的CBCT影像XVI,測量假體為Catphan phantom CTP404。經由迭代式濾波器(Iterative Filter)分析不同材質在螢幕上所顯示的灰階,以辨識假體內聚苯乙烯(Polystyrene, C8H8, PS)與壓克力(Acrylic)材質,同時比較手動WL調整和迭代式濾波器的差異。結果:PS材質在視覺上已可以辨識,所以在迭代式濾波器的P值(1.0 x 10^(-13))與手動WL的P值(2.5 x 10^(-12))都很小。而壓克力材質在視覺上辨識度不佳,在迭代式濾波器的P值(6.88 x 10^(-6))明顯優於手動調整WL的P值(0.026),迭代式濾波器在統計上有較佳的辨識度。結論:在本研究中發現,人工手動之WL並無法改善影像品質。本研究方法具有高通之「迭代式濾波器」,能快速將選取區域的影像調整到最佳,不會因個人經驗而有差異。對於不易鑑別的區域,此濾波器設計在統計上影像有更好的辨識度。本研究的結果顯示「迭代式濾波器」在不犧牲影像品質甚至有改善下,提供更適切的自動WL來滿足視覺辨識性。未來希望將此技術應用於不易被鑑別的臨床醫學影像。 |
英文摘要 | Purpose: The purpose of this study is to identify the difference in the window and level of the CBCT (Cone Beam Computed Tomography) image between manual adjustment and Iterative Filter. By Iterative Filter, the best contrast image is displayed immediately and the identification of the material in the CBCT is improved.Materials and Methods: The instrument used in this study is the CBCT image XVI of the Elerkta Synergy linear. Catphan phantom CTP 404 is the material being measured. The gray scale on the monitor is analyzed through Iterative Filter to identify Polystyrene, C8H8, PS and Acrylic in the specimen. The gray scale of different materials showed on the monitor can be analyzed and Iterative Filter, the sesult is also compare with manual adjustment.Results: PS material can be identified visually; therefore, the P value of the Iterative Filter (1.0 × 10^(-13)) and the P value of the manual adjustment (2.5 × 10^(-12)) are trivial. However, because it is more difficult to identify the Acrylic material in the image, the P value of the Iterative Filter (6.88 × 10^(-6)) is significantly better than the P value of the manual adjustment (0.026). The Iterative Filter has better identification.Conclusion: In this study, the manual adjustment to the window and level can not improve the quality of the image. The approach in this study using the Iterative Filter can perfect the chosen image immediately. The image quality in window-level and identification can be improved Iterative Filter Statistic Method and has a better result than that of manual adjustment. The Iterative Filter provides adequate identification and the improvement in the image quality. |
本系統之摘要資訊系依該期刊論文摘要之資訊為主。