研究學者廣泛地利用美國心臟協會的心肌標準化分區(AHA-17)來計算一些臨床應用，例如：心肌微灌流、左心室機能以及冠狀動脈的解剖。然而研究學者手動圈選感興趣區域並計算該區域之平均T1值，必須重複時十七次以重建AHA-17之牛眼示意圖。對於大量資料庫之研究，此圈選流程非常耗時且繁瑣。本研究因此提出應用在短軸切面之心肌T1磁振造影的左心室自動分割運算。在本研究中，自動影像切割分成兩部分，分別是左心室血液以及心肌區域的切割。我們使用合成影像法來實現左心室血液區域切割，並利用左心室切割的結果獲取心肌切割遮罩並以本研究提出的心肌層長法來改善此心肌切割結果。統計結果顯示，合成影像及心肌層長法提高了切割的準確率。在臨床應用上，未來可能成為自動切割心肌T1磁振造影的可靠工具，亦能減少繁瑣手動圈選流程。

Researchers widely utilized a standardized myocardial segmentation of American Heart Association (AHA-17) to measure myocardial perfusion, functions of left ventricle, and coronary functions for clinical investigations. However, to achieve AHA-17 segmentation, in general, researchers manually select the region-of-interests (ROI) and calculate the average T1 values of the ROI. The procedure has to be repeated 17 times to reconstruct the AHA-17 diagram. It is a time-consuming task for researchers of large-scale databases. This study presents an automatic segmentation for the cardiac magnetic imaging of short-axis in modified Look-Locker inversion recovery (MOLLI) data sets. In this study, the automatic segmentation is divided into two parts, the segmentation of the LV blood pool region and the LV walls. We used an image- synthesis method and layer-growing method to improve the segmentation accuracy. Results demonstrated the accuracy of the obtained myocardium mask is significantly improved by using the layer-growing method. In summary, this study presents a practical and robust tool for application of automatic myocardium segmentation for MOLLI data sets.

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