基于聚類分析提取DSC-MRI腦灌注的動脈輸入函數
發(fā)布時間：2018-11-15 23:41
【摘要】：使用內源性對比劑的動態(tài)敏感對比磁共振成像(Dynamic Susceptibility Contrast-Magnetic Resonance Imaging, DSC-MRI)已經廣泛應用于腦灌注加權成像,可用于測量腦血流、腦血容積、平均通過時間等與腦血液動力學參數有關的生理指標,在臨床疾病診斷和治療方案選擇方面發(fā)揮著重要作用,因此DSC-MRI快速、準確和魯棒的定量計算對臨床實踐意義重大。在利用DSC-MRI技術進行腦血液動力學參數定量時,需事先獲得動脈輸入函數,動脈輸入函數的準確性將直接影響最終計算結果。傳統情況下,動脈輸入函數的提取依賴經驗豐富的放射醫(yī)師手工選取大腦中動脈或頸內動脈的若干像素點來實現,然而該種手動方法耗時較長,且對操作者依賴,導致不同操作者間和同一操作者不同時間點間的結果缺乏可重復性,同時由于DSC-MRI圖像的空間分辨率相對較低,基于手動方法提取動脈輸入函數的結果也會受到部分容積效應的嚴重污染。因此,開發(fā)能夠減少人為干預的自動或半自動動脈輸入函數提取算法成為一個迫切需要解決的現實問題。為了解決手動提取動脈輸入函數的弊端,本研究評估了不同簇分析算法提取DSC-MRI腦灌注中動脈輸入函數的效能。具體步驟包括：采集42位健康志愿者的DSC-MRI腦灌注加權圖像；利用離線工作站校正由于呼吸、心跳、被試者難以控制的不自主運動或轉動造成各相位容積圖像不對齊的情況；通過手工瀏覽方式選擇首幅容積圖像中含右水平大腦中動脈的掃描層面；將所選層面圖像信號的時間-強度曲線轉化為對比劑的時間-濃度曲線；刪除曲線下面積較小的曲線、震蕩頻率較嚴重的曲線和受部分容積效應污染嚴重的曲線；最后,將各種簇分析技術應用于剩余曲線,自動提取動脈輸入函數,并比較各種簇分析算法在計算動脈輸入函數方面的準確性、可重復性及復雜度。由于臨床實驗缺乏金標準的支持,因此本研究還增添了模擬實驗部分,通過估計的動脈輸入函數和真實的動脈輸入函數的比較,評估各種簇分析算法在檢測動脈輸入函數方面的可行性。實驗結果表明,(1)不可重復的聚類算法：k均值簇分析算法相對手工方法而言能夠獲得更準確的動脈輸入函數,用時也更短；相對模糊c均值算法而言,k均值算法能夠獲得更準確的計算結果且具有更好的可重復性；(2)可重復的聚類算法：相對快速仿射傳播聚類算法而言,歸一化分割聚類算法和凝聚層次聚類算法都可以獲得更準確的動脈輸入函數,但是歸一化分割聚類算法比凝聚層次聚類算法的計算復雜度更低,因此具有更好的應用前景。
[Abstract]:Dynamic sensitive contrast magnetic resonance imaging (Dynamic Susceptibility Contrast-Magnetic Resonance Imaging, DSC-MRI) using endogenous contrast agents has been widely used in cerebral perfusion weighted imaging, which can be used to measure cerebral blood flow, cerebral blood volume, The mean pass time and other physiological indexes related to cerebral hemodynamic parameters play an important role in the diagnosis of clinical diseases and the selection of treatment schemes. Therefore, the rapid, accurate and robust quantitative calculation of DSC-MRI is of great significance in clinical practice. When using DSC-MRI technique to quantify the cerebral hemodynamic parameters, the arterial input function should be obtained in advance, and the accuracy of the arterial input function will directly affect the final calculation results. Traditionally, the extraction of arterial input function depends on experienced radiologists to manually select several pixels of the middle cerebral artery or internal carotid artery. However, the manual method is time-consuming and dependent on the operator. The results between different operators and different time points of the same operator are lack of repeatability, and the spatial resolution of DSC-MRI images is relatively low. The result of extracting arterial input function based on manual method will also be seriously polluted by partial volume effect. Therefore, the development of automatic or semi-automatic arterial input function extraction algorithm, which can reduce human intervention, has become an urgent need to solve the practical problem. In order to solve the problem of manually extracting arterial input function, the effectiveness of different cluster analysis algorithms for extracting arterial input function in cerebral perfusion of DSC-MRI was evaluated. The concrete steps include: collecting DSC-MRI perfusion weighted images of 42 healthy volunteers; using off-line workstation to correct the unaligned images of each phase volume caused by involuntary movement or rotation which is difficult for the subjects to control because of breathing, heartbeat and difficulty. The scanning plane with right horizontal middle cerebral artery in the first volume image was selected by manual browsing, and the time-intensity curve of the selected plane image signal was transformed into the time-concentration curve of contrast medium. Delete the curve with smaller area under the curve, the curve with more serious oscillation frequency and the curve polluted seriously by partial volume effect; Finally, various cluster analysis techniques are applied to the residual curve to extract the arterial input function automatically, and the accuracy, repeatability and complexity of the various cluster analysis algorithms in calculating the arterial input function are compared. Due to the lack of gold standard support in clinical trials, this study also adds a simulation experiment to compare the estimated arterial input function with the real arterial input function. To evaluate the feasibility of various cluster analysis algorithms in detecting arterial input functions. The experimental results show that: (1) the non-repeatable clustering algorithm: the k-means cluster analysis algorithm can obtain more accurate arterial input function than manual method, and the time is shorter; Compared with the fuzzy c-means algorithm, the k-means algorithm can obtain more accurate results and has better repeatability. (2) repeatable clustering algorithm: compared with fast affine propagation clustering algorithm, both normalized segmentation clustering algorithm and condensed hierarchical clustering algorithm can obtain more accurate arterial input function. But the computational complexity of the normalized segmentation clustering algorithm is lower than that of the condensed hierarchical clustering algorithm, so it has a better application prospect.
【學位授予單位】：東北大學
【學位級別】：博士
【學位授予年份】：2015
【分類號】：R445.2;R318.04
本文編號：2334743
[Abstract]:Dynamic sensitive contrast magnetic resonance imaging (Dynamic Susceptibility Contrast-Magnetic Resonance Imaging, DSC-MRI) using endogenous contrast agents has been widely used in cerebral perfusion weighted imaging, which can be used to measure cerebral blood flow, cerebral blood volume, The mean pass time and other physiological indexes related to cerebral hemodynamic parameters play an important role in the diagnosis of clinical diseases and the selection of treatment schemes. Therefore, the rapid, accurate and robust quantitative calculation of DSC-MRI is of great significance in clinical practice. When using DSC-MRI technique to quantify the cerebral hemodynamic parameters, the arterial input function should be obtained in advance, and the accuracy of the arterial input function will directly affect the final calculation results. Traditionally, the extraction of arterial input function depends on experienced radiologists to manually select several pixels of the middle cerebral artery or internal carotid artery. However, the manual method is time-consuming and dependent on the operator. The results between different operators and different time points of the same operator are lack of repeatability, and the spatial resolution of DSC-MRI images is relatively low. The result of extracting arterial input function based on manual method will also be seriously polluted by partial volume effect. Therefore, the development of automatic or semi-automatic arterial input function extraction algorithm, which can reduce human intervention, has become an urgent need to solve the practical problem. In order to solve the problem of manually extracting arterial input function, the effectiveness of different cluster analysis algorithms for extracting arterial input function in cerebral perfusion of DSC-MRI was evaluated. The concrete steps include: collecting DSC-MRI perfusion weighted images of 42 healthy volunteers; using off-line workstation to correct the unaligned images of each phase volume caused by involuntary movement or rotation which is difficult for the subjects to control because of breathing, heartbeat and difficulty. The scanning plane with right horizontal middle cerebral artery in the first volume image was selected by manual browsing, and the time-intensity curve of the selected plane image signal was transformed into the time-concentration curve of contrast medium. Delete the curve with smaller area under the curve, the curve with more serious oscillation frequency and the curve polluted seriously by partial volume effect; Finally, various cluster analysis techniques are applied to the residual curve to extract the arterial input function automatically, and the accuracy, repeatability and complexity of the various cluster analysis algorithms in calculating the arterial input function are compared. Due to the lack of gold standard support in clinical trials, this study also adds a simulation experiment to compare the estimated arterial input function with the real arterial input function. To evaluate the feasibility of various cluster analysis algorithms in detecting arterial input functions. The experimental results show that: (1) the non-repeatable clustering algorithm: the k-means cluster analysis algorithm can obtain more accurate arterial input function than manual method, and the time is shorter; Compared with the fuzzy c-means algorithm, the k-means algorithm can obtain more accurate results and has better repeatability. (2) repeatable clustering algorithm: compared with fast affine propagation clustering algorithm, both normalized segmentation clustering algorithm and condensed hierarchical clustering algorithm can obtain more accurate arterial input function. But the computational complexity of the normalized segmentation clustering algorithm is lower than that of the condensed hierarchical clustering algorithm, so it has a better application prospect.
【學位授予單位】：東北大學
【學位級別】：博士
【學位授予年份】：2015
【分類號】：R445.2;R318.04
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