非酒精性脂肪肝臟疾病(Nonalcoholic Fatty Liver Disease, NAFLD)之超音波影像為非侵入性檢查，除可接受度較高，亦可排除侵入式切片的人為經驗不足與主觀判斷造成誤診情形。因此如何對超音波影像的組織進行更精確之變異程度分析，並能以超音波非侵入式為主，以多種判斷數據加以驗證其結果，診斷出之結果將會大幅提高其可靠性。
本研究使用衰退係數(attenuation coefficient, ATN)、積分散射係數(integrated backscatter, IB)與聲學結構量化(acoustic structure quantification, ASQ)三種定量技術於小鼠模型中進行正常肝組織(normal liver, NL)與脂肪肝組織的分辨。我們先利用實驗與模擬對聚氫乙烯基醇組織仿體量測ATN與IB，並探討聚焦深度對其評估之準確性。小鼠動物模型則進行12周高脂食物餵食誘發非酒精性脂肪肝臟疾病，由超音波影像進行組織定量參數估計，並於實驗組與控制組間進行比較。
實驗結果顯示於NL組有較低衰退係數 2.38 0.19 (dB/cm-MHz)，NAFLD組有較高衰退係數 4.15 0.14 (dB/cm-MHz)；衰退補償估算NL組與NAFLD組之IB值分別為-5.28 1.88 (dB)、23.21 4.42 (dB)；ASQ參數則分別為2.29 0.29與1.54 0.11。結果驗證此三種參數能以顯著差異區分NL組與NAFLD組，參數間可互相參照以提高NAFLD之超音波影像診斷效能。

Conventionally, ultrasound diagnosis of nonalcoholic fatty liver disease (NAFLD) relies on the physician’s judgement on texture and brightness of liver tissue in B-mode imaging. In this study, quantitative acoustic parameters such as statistical characteristics of signal envelope (ASQ), integrated backscattering coefficient (IB) and attenuation coefficient (ATN) are utilized to differentiate the NAFLD liver from the normal liver. For attenuation coefficient, the estimation accuracy significantly changes with the ROI spacing and the ROI position relative to the transmit focal zone. Our results indicate that a pre-focus setting can reduce the bias of measured attenuation coefficient to 4.82% and 2.35%, respectively in phantom experiment and simulation. A larger ROI spacing can further improve the accuracy of attenuation estimation. Animal experiments are performed by feeding mice with normal diet (NL group) and high-fat diet (HF group) for three months and then acquiring ultrasound images of mice at 20 MHz for estimation of acoustic parameters. Compared to the NL group, the HF group exhibits higher IB and higher ATN together with lower ASQ. The difference of IB, ATN and ASQ between the NL and HF groups is all statistically significant (p < 0.05). Therefore, integration of these acoustic parameters may provide a reliable index for quantitative NAFLD diagnosis.

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