近年來由心血管疾病在國內十大死因前五名中就佔了兩位﹐儼然已成為現代人的主要殺手之一。而動脈硬化是導致心血管疾病的主要原因，因此醫界研究預防及治療動脈硬化，向來不遺餘力。過去臨床診斷上有X光血管攝影術及光纖血管內視鏡，不過這兩種方法都不能得到血管組織層的影像，無法做更進一步的診斷。近幾年來，血管內超音波(Intravascular Ultrasound，IVUS)的發展，提供臨床上在體內評估血管壁的變化，可直接讓醫師看到血管的剖面結構，得到血管內各種結構之狀態資訊。
然而欲分析血管內超音波影像必須先對血管的邊界進行搜索，而手動方式進行血管邊界的描繪常會花許多時間。故本研究的目的之一為發展一能取代手動描繪的邊界搜尋方法，期望能更快速且客觀的搜尋出IVUS影像中的邊界，提供血管幾何結構上的定量描述。此外，我們利用雙平面血管X光攝影的影像來提供血管在空間中的位置及曲度，重建探頭導管的軌跡。並且搭配血管內超音波序列影像重建具有空間曲度之三維血管影像。
本研究共分析了4位孩童，包含14段血管內超音波影像資料，結果顯示自動血管管腔邊界描繪和手動描繪的結果，兩者之相關性高達到0.92以上。本研究所提出的搜索法確實能取代手繪邊界的描繪方法。而三度空間血管虛擬影像的重建，能清楚的描述血管的管徑大小、空間位置及曲度變化，提供醫師在診斷血管病變上更多的參考。.

Vascular diseases have become the major causes of death for the human beings. According to recent investigations, two vascular diseases were listed in the top five of the ten major causes of death in Taiwan. The progression of atherosclerosis is the main indication for the severity of the vascular diseases. In the past years angiogram and fiberoptic angioscope were widely applied in clinically diagnosing the diseases, but both were unable to get the cross-section images of the vessel wall. Intravascular ultrasound (IVUS) is a powerful tool with great potential in characterizing the structure of the normal and the atherosclerotic regions of arteries, which allows doctors to easily identify plaques which lead to various clinical syndromes.
The technique of boundary detection is crucial for tissue characterization in IVUS images. The vessel contour can be obtained by manually tracing the image, which, however, is extremely laborious. Thus, the first objective of this study was to develop a fast and subjective boundary search method for replacing the manual one. In addition, we reconstructed the catheter trajectory by using biplane angiography and then a 3-D vessel model was built by fusing the trajectory with the sequential IVUS images.
Four children were tested with totally fourteen IVUS sequential images were acquired and analyzed in this study. By comparing the arterial lumen boundaries detected according to the procedures proposed in this study with the ones traced manually by expert observers, the detected boundaries show great agreement for two methods (r>=0.92). The result demonstrates that the proposed automatic algorithm is efficient in arterial boundary detection of IVUS images. The reconstructed 3-D vessel model provides useful clinical information for helping doctors in evaluating vascular diseases..

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