研究學者提出以心肌胞外比容 (extracellular volume fraction, ECV)映像來定量觀察心肌纖維化的程度，得到很好的結果。要計算出心肌胞外比容映像，需要在注射顯影劑前後取得至少兩組影像。藉由觀察心肌各像素的T1改變再搭配血液檢查中的血比容量測，來計算位對該像素的心肌胞外比容。然而這項計算成功的前提在於顯影劑注射前後所取得的影像像素位置完全相同，這在實際應用中幾乎無法達成，因此需仰賴影像對位運算來修正像素位移。然而，顯影劑注射前後之心臟影像對比有很強的改變，不利於對位運算。本研究提出利用合成影像的對位演算法，應用於心肌胞外比容映像，與過往研究者所提出的對位方式比較，對位準確程度明顯地提升。本論文的結論為基於合成影像的影像形變對位法，減少心肌像素偏移，因此能夠重建出更精準的心肌胞外比容映像。

Previous researchers used extracellular volume fraction (ECV) in the myocardium to quantify myocardial fibrosis. This method has been shown practical in clinical practice. Two myocardial T1 maps acquired before and after contrast administration are required to reconstruct a myocardial ECV map. The ECV value of each pixel is calculated based on the hematocrit and the alteration of T1 in the myocardium and blood. For pixel-by-pixel ECV map calculation, precise image registration is crucial to obtain accurate myocardial ECV value. However, the image contrasts obtained before and after the administration of the contrast agent are prominently different. The contrast change is a challenge for the algorithm of image registration. This thesis proposed a registration method using synthetic image estimation. Compared to previous investigations, the proposed method significantly improves the accuracy of the image registration. This thesis concluded that the accuracy of ECV mapping can be improved by using the proposed algorithm.

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