【摘要】：肺4D-CT圖像為放射治療提供了全面的圖像引導(dǎo),它可以清楚地反映肺部靶區(qū)器官隨呼吸的運(yùn)動規(guī)律,同時減少呼吸運(yùn)動引起的偽影,為當(dāng)今的肺癌治療提供了巨大的幫助。然而,肺4D-CT數(shù)據(jù)的采集時間長,劑量大,因此不能實現(xiàn)薄層厚的掃描,這導(dǎo)致采集到的肺4D-CT圖像層間分辨率顯著低于層內(nèi)分辨率。因此想要觀察正常分辨率的肺4D-CT的Z軸圖像,必須要對的Z軸圖像按照一定比例進(jìn)行插值。常用的插值方法有線性插值,三次樣條插值等,但是這些插值的方法都不能得到理想的重建結(jié)果。本文為了提高肺4D-CT圖像的多平面顯示的質(zhì)量,研究了基于圖像超分辨率重建的方法,來重建高分辨率的肺4D-CT圖像。圖像超分辨率重建技術(shù)的基本思想是利用單幅或者多幅圖像的信息來提高圖像的顯示質(zhì)量。常用的方法有基于頻域的方法和基于空域的方法�；陬l域的方法不易擴(kuò)展,且不易添加約束。因此目前的研究主要集中在基于空域的超分辨率重建方法研究。本文針對肺4D-CT圖像特性,研究了兩種基于空間域處理的肺4D-CT圖像超分辨率重建:基于多相位圖像相似性和基于全局圖割的超分辨率重建方法。而且重建結(jié)果在視覺和量化方面,均優(yōu)于傳統(tǒng)的插值算法和POCS算法。本文的前兩章分別介紹了研究意義和相關(guān)技術(shù)現(xiàn)狀。在第三章和第四章分別詳細(xì)介紹了基于多相位相似性和基于全局圖割的超分辨率重建方法。其中第三章闡述了非局部均值濾波,基于多相位相似性的圖像塊搜素,以及全局約束重建。第四章闡述了全局圖的構(gòu)造,能量函數(shù)構(gòu)建及優(yōu)化求解。本文第五章對全文進(jìn)行了總結(jié)和展望。
[Abstract]:Lung 4D-CT images provide a comprehensive image guide for radiotherapy, which can clearly reflect the movement of lung target organs with respiration, and reduce the artifacts caused by respiratory movements, which provides a great help for the treatment of lung cancer today. However, the collection time of lung 4D-CT data is long and the dose is large, so it is not possible to realize thin-layer thick scanning, which leads to the interlayer resolution of collected lung 4D-CT images being significantly lower than that of intralayer resolution. Therefore, in order to observe Z-axis images of lung 4D-CT with normal resolution, the Z-axis images must be interpolated in a certain proportion. The commonly used interpolation methods include linear interpolation, cubic spline interpolation and so on, but none of these interpolation methods can obtain ideal reconstruction results. In order to improve the quality of multiplanar display of lung 4D-CT images, a method based on super-resolution reconstruction is studied to reconstruct high-resolution lung 4D-CT images. The basic idea of image super-resolution reconstruction is to improve the display quality of images by using the information of single or multiple images. The commonly used methods are frequency-based and spatial-based methods. The method based on frequency domain is not easy to extend, and it is difficult to add constraints. Therefore, the present research focuses on spatial-based super-resolution reconstruction. According to the characteristics of lung 4D-CT images, two super-resolution reconstruction methods based on spatial domain processing for lung 4D-CT images are studied in this paper: one is based on the similarity of multi-phase images and the other is based on global image cutting. Moreover, the reconstruction results are superior to the traditional interpolation algorithm and POCS algorithm in vision and quantization. The first two chapters of this paper respectively introduce the significance of the research and the current situation of related technologies. In chapter 3 and chapter 4, the super-resolution reconstruction method based on multi-phase similarity and global graph cut is introduced in detail. In the third chapter, non-local mean filter, image block search based on multi-phase similarity, and global constrained reconstruction are discussed. In chapter 4, the construction of global graph, the construction of energy function and the optimization solution are discussed. The fifth chapter summarizes and prospects the full text.
【學(xué)位授予單位】：南方醫(yī)科大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R734.2;TP391.41

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