本文選題：肝臟4D + DCE-MRI��； 參考：《浙江大學(xué)》2017年碩士論文

【摘要】：肝細(xì)胞癌是異質(zhì)性最強(qiáng)、死亡率最高的惡性腫瘤之一。在肝硬化結(jié)節(jié)逐步形成肝細(xì)胞癌的過程中,新生異常動(dòng)脈血供的增多是臨床診斷的一個(gè)重要判據(jù)。肝臟四維動(dòng)態(tài)對(duì)比增強(qiáng)磁共振成像(DCE-MRI:Dynamic Contrast-Enhanced Magnetic Resonance Imaging)不僅可提供病變組織形態(tài)學(xué)的信息,還可反映病變組織血流微循環(huán)的改變,從而為肝細(xì)胞癌的早期診斷及療效評(píng)估提供更加豐富、精準(zhǔn)的影像信息。肝臟4D DCE-MRI的臨床應(yīng)用主要面臨兩大技術(shù)挑戰(zhàn):一是快速成像,需要基于高度降采樣的k空間數(shù)據(jù),重建高時(shí)空分辨率的全肝4D圖像;二是呼吸運(yùn)動(dòng)校正,需要有效去除長(zhǎng)時(shí)間掃描中呼吸運(yùn)動(dòng)引起的偽影。本文重點(diǎn)研究自由呼吸下的高時(shí)空分辨率全肝4D DCE-MRI的關(guān)鍵技術(shù),主要取得了如下創(chuàng)新成果:(1)針對(duì)基于高度降采樣的A空間數(shù)據(jù)重建高時(shí)空分辨率圖像的需求,提出了一種基于字典學(xué)習(xí)的低秩稀疏分解重建算法。采用低秩稀疏分解模型有效挖掘重建圖像幀之間的相關(guān)性,利用字典學(xué)習(xí)進(jìn)一步增強(qiáng)算法對(duì)數(shù)據(jù)的自適應(yīng)性,從而實(shí)現(xiàn)了圖像信號(hào)的高稀疏性表達(dá)。實(shí)驗(yàn)表明,本文算法可以有效去除降采樣偽影,改善重建圖像細(xì)節(jié)。(2)面向自由呼吸掃描,提出了一種內(nèi)嵌呼吸運(yùn)動(dòng)校正的動(dòng)態(tài)MRI重建算法。首先,基于A空間數(shù)據(jù)直接估計(jì)一維呼吸運(yùn)動(dòng)信號(hào),并據(jù)此信號(hào)k將空間數(shù)據(jù)劃分到若干運(yùn)動(dòng)狀態(tài)中;然后,基于各狀態(tài)的A空間數(shù)據(jù),采用低秩稀疏分解分別重建圖像序列;最后,采用稀疏性約束對(duì)整個(gè)序列做二次重建。實(shí)驗(yàn)表明,本文算法可以有效去除運(yùn)動(dòng)偽影,重建自由呼吸下的高時(shí)空分辨率圖像。(3)針對(duì)胸腹部圖像的呼吸運(yùn)動(dòng)配準(zhǔn)問題,提出了 一種保持運(yùn)動(dòng)場(chǎng)不連續(xù)性的配準(zhǔn)算法。采用基于馬爾可夫隨機(jī)場(chǎng)的離散優(yōu)化方法,結(jié)合運(yùn)動(dòng)場(chǎng)估計(jì)值和圖像信號(hào)自動(dòng)分割得到內(nèi)臟與胸腹部?jī)?nèi)壁之間呼吸滑動(dòng)的界面,然后去除橫跨界面的運(yùn)動(dòng)場(chǎng)平滑性約束。實(shí)驗(yàn)表明,本文算法可以實(shí)現(xiàn)呼吸滑動(dòng)界面的自動(dòng)分割,有效降低配準(zhǔn)誤差。
[Abstract]:Hepatocellular carcinoma (HCC) is one of the most heterogeneous malignant tumors with the highest mortality. The increase of abnormal arterial blood supply is an important criterion for clinical diagnosis of hepatocellular carcinoma (HCC). DCE-MRI: dynamic Contrast-Enhanced Magnetic Resonance imagingcan not only provide information of pathological morphology, but also reflect the changes of blood flow microcirculation in pathological tissues, thus providing more valuable information for early diagnosis and evaluation of curative effect of hepatocellular carcinoma. Accurate image information. The clinical application of liver 4D DCE-MRI faces two major technical challenges: one is rapid imaging, which requires reconstruction of the whole liver 4D image with high spatiotemporal resolution based on highly decimated k spatial data, and the other is respiratory motion correction. It is necessary to effectively remove artifacts caused by respiratory movement during long-term scanning. This paper focuses on the key technologies of high spatiotemporal resolution 4D DCE-MRI under free breathing. The main achievements are as follows: 1) the requirement of reconstruction of high spatiotemporal resolution images based on A spatial data based on highly decimated sampling. A low rank sparse decomposition reconstruction algorithm based on dictionary learning is proposed. The low rank sparse decomposition model is used to effectively mine the correlation between reconstructed image frames, and the dictionary learning is used to further enhance the adaptability of the algorithm to the data, thus realizing the high sparsity representation of image signals. Experimental results show that the proposed algorithm can effectively remove the subsampling artifacts and improve the image details for free breathing scanning. A dynamic MRI reconstruction algorithm with embedded breathing motion correction is proposed. Firstly, the one-dimensional breathing motion signal is directly estimated based on A spatial data, and then the spatial data is divided into several moving states based on the signal k, and then the image sequence is reconstructed by low-rank sparse decomposition based on the A-space data of each state. Finally, the sparse constraint is used to reconstruct the whole sequence twice. Experimental results show that the proposed algorithm can effectively remove motion artifacts and reconstruct high spatial and temporal resolution images under free breathing. To solve the problem of respiratory motion registration in chest and abdomen images, a registration algorithm to maintain discontinuity of sports fields is proposed. A discrete optimization method based on Markov random field is used to segment automatically the respiratory sliding interface between the viscera and the inner wall of the chest and abdomen by combining the estimation value of the sports field and the image signal. Then the smoothness constraint of the motion field across the interface is removed. Experiments show that the proposed algorithm can realize automatic segmentation of respiratory sliding interface and reduce registration error effectively.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R735.7;TP391.41

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本文編號(hào)：1779303