本文選題：自適應放療　切入點：錐形束CT　出處：《南方醫(yī)科大學》2017年博士論文

【摘要】：放射治療是治療腫瘤的三大有效手段之一,其治療效果取決于兩個方面:一是保證充分的靶區(qū)受照劑量,二是減少正常組織受照劑量。放療技術也正是以此為目標向前發(fā)展,從三維適形放療(three dimensional conformal radiotherapy,3D-CRT)到調強放療(intensity-modulated radiotherapy,IMRT),再到圖像引導放療(image-guided radiotherapy,IGRT)以及自適應放療(adaptive radiotherapy,ART)。具體而言,3D-CRT實現(xiàn)射野與靶區(qū)在形狀上的一致,IMRT在此基礎上實現(xiàn)劑量適形,而IGRT利用治療中(in-room)影像校正了每治療分次的擺位誤差。但是,由于治療效應和正常生理過程的影響,某些感興趣區(qū)域(region of interest,ROI)的位置、形狀和體積在治療期間會發(fā)生變化,進而可能導致腫瘤欠照射或危及器官(organ at risk,OAR)過照射。ART通過重新設計或調整計劃來適應當前的解剖結構情況,是解決上述問題的一種有效手段。作為ART中的關鍵技術之一,圖像變形配準(deformable image registration,DIR)算法關聯(lián)著計劃圖像和in-room圖像,關聯(lián)的準確程度決定了 ART的有效性和可靠性。因此驗證DIR的精度是非常必要的。本文以此為出發(fā)點開展了一系列相關研究。本文首先回顧并實現(xiàn)四類(十種)代表性的基于圖像灰度的DIR算法。這四類算法分別是光流場類(HS,HSLK和FFD)、demons類(OD,MD,SFD,DFD和DISC)、水平集類(LS)和樣條類(BSpline),其中DISC是在圖像處理單元(graphic processing unit,GPU)上實現(xiàn),其他 DIR 算法均用 Matlab 實現(xiàn)。針對關于多模態(tài)圖像驗證DIR算法精度的研究相對缺乏的問題,本文回溯性地采集21例鼻咽癌患者的CT和CBCT圖像,由經驗豐富的醫(yī)生勾畫雙側腮腺、雙側頜下腺、頸椎椎體和椎孔四種ROI。然后利用所實現(xiàn)的DIR算法將CT圖像上的ROI輪廓線推衍到CBCT圖像上,并與醫(yī)生勾畫的真實輪廓線進行比較,以此評估其在ROI輪廓線推衍方面的精度和性能差異。結果表明,所有DIR算法的表現(xiàn)并不一定好于剛性配準。一般來說,DIR算法在剛性結構上的表現(xiàn)要比其在軟組織上的表現(xiàn)出色,且DIR精度還隨ROI的不同而變化。另外,算法的表現(xiàn)還與ROI的形變程度(時間推移)相關。針對目前體模驗證研究中體模復雜度缺乏的問題,本文以真實患者的腹部CT影像為參考,設計并制備一種高仿真的物理體模。該體模包含肝、腎、脾、胃和脊椎等多種仿真結構,這些結構具有與真實解剖結構相似的體積、形狀、CT值以及空間位置。另外,體模還包含195個標記點(直徑為1～2 mm的金屬球),均勻分布于各仿真器官的內部、表面以及器官間區(qū)域,標記點在變形前后的位置變化被作為精度驗證的標準。體模驗證結果表明,絕大多數(shù)DIR算法能顯著改善剛性配準的精度,其表現(xiàn)與圖像對比度及ROI性質(體積、形狀等)等因素有關。盡管器官表面的DIR精度在總體上要顯著高于器官內部的DIR精度,但對于同一實質器官而言,表面與內部之間一般不存在顯著的精度差異。本文在DIR算法驗證方面取得了一些初步成果,但在某些細節(jié)上仍有待進一步的研究,比如各DIR算法實現(xiàn)過程中參數(shù)優(yōu)化問題、臨床數(shù)據(jù)驗證中勾畫誤差量化問題以及物理體模驗證中基準標記點分布問題等等。此外,根據(jù)驗證結果來改進現(xiàn)有DIR算法將會是我們未來的研究方向。例如,本研究表明算法精度與ROI的對比度有關,據(jù)此我們可人為提高原圖像中某些ROI的對比度,將局部對比度增強后的圖像的配準結果作為先驗信息或約束條件加入到原圖像的配準中,從而提高原圖像的配準精度。
[Abstract]:Radiotherapy is one of the three effective methods to treat cancer, its treatment effect depends on two aspects: one is to ensure the target full dose, two is to reduce the dose of normal tissue. Radiotherapy technology is for this target forward, from three-dimensional conformal radiotherapy (three dimensional conformal radiotherapy, 3D-CRT in intensity-modulated radiotherapy (intensity-modulated) radiotherapy, IMRT), and then to image guided radiotherapy (image-guided radiotherapy, IGRT (adaptive radiotherapy) and adaptive radiotherapy, ART). Specifically, 3D-CRT fields and target zone is consistent in shape, the dose of conformal IMRT on the basis of this, and use in the treatment of IGRT (in-room) the image correction setup error in each treatment time. However, due to the influence of treatment effect and the normal physiological process, some regions of interest (region of, interest, ROI) the location, shape and body Product will change during treatment, which may lead to tumor under irradiation or organs (organ at risk, OAR) after irradiation.ART through redesign or adjust the plan to meet the current situation of the anatomical structure, is an effective means to solve the above problems. As one of the key technologies in ART, image registration (deformation deformable image registration, DIR) algorithm associated with the program image and in-room image, the accurate degree of correlation determines the validity and reliability of ART. It validates the accuracy of the DIR. It is very necessary to carry out a series of related research in this article as a starting point. This paper firstly reviews and realize the four (ten) representative DIR algorithm based on image gray value. These four algorithms are opticalflow class (HS, HSLK and FFD), Demons (OD, MD, SFD, DFD and DISC), the level set class (LS) and spline type (BSpline), which is DISC in image processing Unit (graphic processing unit, GPU) on the implementation of other DIR algorithms are implemented in Matlab. According to the relative lack of research on multimodal image authentication algorithm DIR problem, in this paper, a retrospective collection of CT and CBCT images of 21 cases of nasopharyngeal carcinoma patients, by experienced doctors draw the outline of bilateral parotid gland, submandibular glands, cervical vertebra four kinds of ROI. vertebral body and intervertebral foramen and then use the DIR algorithm to achieve the CT image on the ROI contours extrapolated to CBCT images, and the doctor and outline the true contour comparison, in order to evaluate the accuracy and performance of the difference in ROI derived contour aspect. The results show that all the performance of DIR algorithm is not necessarily better than the rigid registration. In general, performance of DIR algorithm in the rigid structure than its performance in the soft tissue on the job, and the accuracy of DIR with ROI also changed. In addition, the performance of the algorithm with ROI The degree of deformation (time). According to the present study phantom verification phantom complexity problem, based on the real patient abdominal CT images as reference, design and preparation of a high physical phantom simulation. The phantom consists of liver, kidney, spleen, stomach and other spinal structure simulation of these, and the real anatomical structure has similar structure shape, size, CT value and spatial position. In addition, the phantom also contains 195 markers (1~2 mm diameter metal ball), internal uniform distribution in each simulation organs, organs and the surface area was used as markers to verify the accuracy of the standard in the position changes before and after deformation. The validation results show that the vast majority of phantom, the DIR algorithm can significantly improve the accuracy of rigid registration, its performance and image contrast and ROI properties (size, shape) and other factors. Although the organ surface in general to show the accuracy of DIR Compared with the internal organs of DIR precision, but for the same substance between the surface and the internal organ, there is generally no significant difference in accuracy. This paper has made some preliminary results in the DIR algorithm, but some details still need to be further studied, such as the DIR algorithm to realize the optimization of parameters in the process of clinical data a validation standard outlined in the quantization error and physical phantom validation marker distribution problems and so on. In addition, our future research direction to improve the existing DIR algorithm will be based on the test results. For example, this study shows that the algorithm accuracy and contrast of ROI, so we can be provided some ROI image contrast in plateau that will be the local contrast enhanced image registration results as a priori information or constraints into the original image in the original registration, so as to improve the precision of image registration.

【學位授予單位】：南方醫(yī)科大學
【學位級別】：博士
【學位授予年份】：2017
【分類號】：TP391.41;R730.55

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本文編號：1659086