【摘要】：動態(tài)增強MRI由于具有無損傷性且可顯示功能信息，在乳腺癌早期診斷和跟蹤治療方面得到了廣泛應(yīng)用。在DCE-MRI檢測時，需要獲取注射造影劑前后的患者乳腺影像。造影劑的使用能夠改變腫瘤區(qū)的原始灰度，進而使得腫瘤與正常組織區(qū)分開來。一般而言，造影劑是通過靜脈來注射的，這樣隨著跟蹤診療過程中影像采集次數(shù)的增加，，造影劑的副作用會在患者體內(nèi)不斷累積。 針對造影劑靜脈注射方法的這一缺陷，從乳頭注射造影劑的新型乳腺動態(tài)增強MRI方法應(yīng)運而生。該方法雖然降低了造影劑的注射量，但由于在造影劑吸收初期，乳頭附近的灰度變化劇烈，為圖像的配準造成了極大困難。本文基于面網(wǎng)絡(luò)配準技術(shù)對該特殊應(yīng)用背景下的圖像配準問題進行了探索，設(shè)計了以球諧分析為核心的面網(wǎng)絡(luò)配準技術(shù)，并輔以適當(dāng)?shù)膱D像預(yù)處理和面配準后的體插值實現(xiàn)圖像層面的配準。 針對造影后圖像靠近乳頭的組織區(qū)域灰度接近背景而難以進行圖像分割的問題，本文設(shè)計了基于灰度數(shù)學(xué)形態(tài)學(xué)的圖像邊緣修復(fù)方法，為較為精確的三維表面重建提供了保證。 面網(wǎng)絡(luò)的配準是由網(wǎng)絡(luò)方位對準和節(jié)點間對應(yīng)關(guān)系的確立這兩步來實現(xiàn)的。本文首先利用保形球映射、規(guī)則采樣和基于貪婪搜索的球諧配準實現(xiàn)造影前后的乳房面網(wǎng)絡(luò)在方位上的配準，而后基于球諧分析的機理運用同球諧基底異球諧系數(shù)重構(gòu)出具有節(jié)點間對應(yīng)關(guān)系的造影前后的乳房面網(wǎng)絡(luò)，從而獲得了表面網(wǎng)絡(luò)的位移場。 針對圖像層面的體配準，本文首先利用均值坐標插值技術(shù)在造影后面網(wǎng)絡(luò)的內(nèi)部插值出相應(yīng)的體位移場，然后據(jù)此應(yīng)用基于線性插值的圖像復(fù)原技術(shù)實現(xiàn)了對原始圖像的幾乎所有像素點的形變位移操作，從而獲得了組織灰度信息清晰可辨的配準圖像。該圖像具有造影前乳腺MRI的形態(tài)，且具有造影后乳腺MRI的組織灰度信息。 該方法的提出為繪制動態(tài)增強（DCE）曲線，從而得到更為全面的診斷信息提供了堅實的基礎(chǔ)。
[Abstract]:Dynamic enhanced MRI has been widely used in early diagnosis and treatment of breast cancer due to its noninvasive and functional information. During DCE-MRI testing, mammograms of patients before and after injection of contrast media are required. The use of contrast agents can change the original grayscale of the tumor area, which distinguishes the tumor from the normal tissue. Generally speaking, the contrast agent is injected through the vein, so as the number of image acquisition increases during the follow-up, the side effects of the contrast agent will accumulate in the patient's body. In view of the defect of intravenous injection of contrast agent, a new dynamic enhanced mammary gland MRI method for nipple injection was proposed. Although this method reduces the amount of contrast agent injected, it is very difficult for image registration because of the sharp change of gray level near the nipple in the early stage of absorption of contrast medium. In this paper, based on the surface network registration technology, the image registration problem under the special application background is explored, and the surface network registration technology with spherical harmonic analysis as the core is designed. The image level registration is realized with proper image preprocessing and volume interpolation after surface registration. In view of the difficulty of image segmentation due to the grayscale of the tissue area near the nipple after angiography, an image edge restoration method based on gray-scale mathematical morphology is designed in this paper, which provides a guarantee for the more accurate 3D surface reconstruction. The registration of surface network is realized by two steps: azimuth alignment of network and establishment of corresponding relation between nodes. In this paper, we first use conformal ball mapping, regular sampling and spherical harmonic registration based on greedy search to realize the azimuth registration of breast face network before and after angiography. Then, based on the mechanism of spherical harmonic analysis, the mammary face network with corresponding relationship between nodes is reconstructed by using the isobaric coefficient of the same spherical harmonic substrate, and the displacement field of the surface network is obtained. For the volume registration of image level, this paper firstly uses the mean coordinate interpolation technique to interpolate the corresponding position field in the interior of the network behind the angiography. Based on this, the image restoration technique based on linear interpolation is applied to realize the deformation displacement operation of almost all pixels of the original image, and the clearly identifiable registration image of the gray level information of the organization is obtained. The image has the shape of mammary MRI before contrast and grayscale information of MRI. This method provides a solid foundation for drawing dynamic enhanced (DCE) curves and obtaining more comprehensive diagnostic information.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：R737.9;R445.2

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