心肌胞外比容的定量計算可以用於評估心肌纖維化，透過磁共振造影儀器掃描得到施予對比劑前後的T1映像，加上經由血液檢查得到的血球容積比，逐一計算出每一個像素的心肌胞外比容。AHA-17模型已廣泛的運用於臨床研究，AHA-17模型是利用牛眼圖來呈現出左心室心肌各區域內的數值，以利找出病灶之處。為了能夠分類是否有法洛氏四合症，透過迴歸分析來訓練及測試資料集，並利用混淆矩陣來得到判別的準確率。本研究提出了全自動化的左心室壁切割及心肌胞外比容的計算，將整個計算系統封裝於雲端運算系統平台，加上自動化切割AHA-17模型與迴歸分析，並應用於區辨法洛氏四合症。

Quantitative estimation of extracellular volume fraction (ECV) allows evaluations of myocardial fibrosis. ECV mapping relies on pixel-by-pixel comparisons of myocardial T1 constants obtained before and after contrast administration and the hematocrit measured by using a blood sample. AHA-17 segments model has been widely used in clinical research. AHA-17 segments model used bullseye to present the values of left ventricular wall is useful for the assessment of myocardial lesion in patients. We aim to discriminate Tetralogy of Fallot (TOF) patients from the control group. Therefore, we use the confusion matrix to calculate the area under the curve of receiver operating characteristic curve (AUC) and used regression analysis and cross-validation to evaluate the regression results. In this thesis, we proposed a fully automatic pipeline to produce ECV and T1 maps and global ECV and T1 values of LV wall. We also combined quantitative analysis with machine learning approaches to distinguish TOF patients from normal controls.

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