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作者(中文):李明儒
論文名稱(中文):超音波特性參數應用於脂肪肝疾病診斷
論文名稱(外文):Diagnosis of fatty liver disease by ultrasound multi-characteristic parameters
指導教授(中文):葉秩光
口試委員(中文):崔柏翔
李夢麟
楊坤澈
學位類別:碩士
校院名稱:國立清華大學
系所名稱:生醫工程與環境科學系
學號:101012542
出版年(民國):103
畢業學年度:103
語文別:中文
論文頁數:104
中文關鍵詞:脂肪肝超音波紋理參數衰減
外文關鍵詞:Nakagami parameter
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脂肪肝為肝臟代謝性能力缺失的初期指標,針對脂肪肝進行診斷與追蹤,能作為嚴重肝臟慢性疾病的警示。肝臟超音波因具有非侵入式與便利性等優點成為最常見的診斷工具。然而,超音波的診斷方式易受限於醫生的主觀意識,因此,許多研究利用半定量法輔助醫生診斷脂肪肝,例如分析肝臟超音波的原始射頻訊號,證實脂肪會改變超音波的聲學特性,或者採用紋理分析辨別脂肪肝的回音特徵,或者以衰減係數描述脂肪肝的嚴重程度等。但目前臨床上仍未有一套完整的標準適用於診斷脂肪肝。
本研究提出結合不同的聲學特性參數以輔助診斷脂肪肝,利用六個紋理特徵參數,即自相關函數、總和平均數、總和變異數、對比、熵及均質性反映脂肪肝超音波影像的各種回音紋理改變,並擷取肝臟超音波的原始射頻訊號,以訊號雜訊比(Signal to noise ratio, SNR)討論脂肪肝的超音波回散射訊號成分,且計算回散射訊號的中心頻率偏移斜率顯示脂肪肝的超音波訊號衰減變化。資料分析共分為兩個階段,第一階段為定量參數的分析能力評估與脂肪肝定量評分標準的制定,其人體資料樣本數共314筆,將診斷效能最好的三個紋理特徵參數以線性判斷分析法統合,並與SNR及中心頻率偏移斜率一起做為脂肪肝定量評分標準的指標,其診斷效率的準確度為72.4%、敏感度為72.2%、特異性為71.8%。第二階段則是驗證臨床使用脂肪肝定量評分標準的能力,其人體資料樣本數共80筆,以隨機取樣的概念進行評估,其診斷效率的準確度為75%、敏感度為71.4%、特異性為77.8%,證實了脂肪肝定量評分標準的可行性。脂肪肝定量評分標準不僅與現行臨床診斷結果有良好的一致性,採取定量的參數也可反映脂肪肝疾病不同的組織特性並排除人為操作的誤差,使結果更加可信。
目錄
第一章 緒論 10
1.1 肝臟代謝性疾病 10
1.2 超音波脂肪肝診斷系統 14
1.3 脂肪肝組織結構特性 20
1.4 脂肪肝對音波的回散射特性 21
1.5 脂肪肝衰減特性 23
1.6 論文架構 25
第二章 材料與方法 27
2.1 紋理特徵參數 27
2.2 訊雜比 32
2.3 中心頻率頻率偏移 34
2.4定量參數影像計算 39
2.5模擬脂肪堆積之仿體實驗 39
2.5.1 不同脂肪濃度的灰階變化情形實驗 46
2.5.2 脂肪改變回散射訊號的組成成分實驗 46
2.5.3 脂肪影響超音波訊號衰減變化實驗 47
2.6 人體資料擷取 48
2.6.1 人體肝臟影像收取 49
2.6.2 血液生化值 51
2.7 肝腎對比 51
2.8 人體脂肪肝定量參數計算 53
2.8.1 建立脂肪肝定量診斷標準 54
2.9 統計分析 55
2.9.1 線性判別分析 55
2.9.2 接收者操作特徵曲線 55
2.9.3 模糊C-means 57
2.10方法總結 58
第三章 結果 59
3.1 仿體分析結果 59
3.1.1 仿體灰階強度變化 59
3.1.2 仿體訊號中SNR變化結果 62
3.1.3 仿體訊號中心頻率變化 65
3.2 人體分析結果 68
3.2.1 肝腎對比診斷FLD的分析能力 68
3.2.2 紋理特徵參數分析脂肪肝的能力 71
3.2.3 SNR分析脂肪肝的能力 79
3.2.4 CFDS slope分析脂肪肝能力 83
3.2.5 FCM群聚分析 86
3.3 定量脂肪肝評分標準 89
3.3.1 數據分布 89
3.3.2 脂肪肝定量診斷標準的分析能力 91
第四章 結論 95
第五章 未來研究方向 97
參考文獻 98
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78. !!! INVALID CITATION !!!
79. Graif M, Yanuka M, Baraz M, Blank A, Moshkovitz M, Kessler A, Gilat T, Weiss J, Walach E, Amazeen P: Quantitative estimation of attenuation in ultrasound video images: correlation with histology in diffuse liver disease. Invest Radiol 2000, 35(5):319-324.
 
 
 
 
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