使用總體經驗模態分解法自動增強超音波影像對比

醫用超音波影像所使用的對比劑是微米等級的微氣泡，而近年來其非線性成像技術已被廣泛研究。然而，微氣泡與組織散射信號的傅立葉頻譜往往不易分離，因此使得對比增強的幅度受限。對此，目前已有研究指出，使用總體經驗模態分解法分解超音波射頻信號將有助於減輕此一問題。總體經驗模態分解法衍生自經驗模態分解法，而經驗模態分解法則是希爾伯特—黃轉換中的核心步驟。有別於傅立葉分析，此系列的信號分析工具均在時域進行運算，不涉及積分轉換，因此可分析非線性、非穩態的信號。總體經驗模態分解法會將一信號分解成若干本質模態函數，而先期研究已發現，某些本質模態函數中微氣泡信號與組織信號的對比會較原信號中的對比更強。但是，此發現在實際應用時仍需使用者指定本質模態函數，況而理想上不應僅選擇一個本質模態函數而損失其他資訊。因此，本研究提出一個新的對比增強機制：使用總體經驗模態分解法分解超音波射頻信號（測試組資料），將分解所得之信號成分加權相加，並於適當頻率解調成像—其中權重與解調頻率由已知微氣泡分布的超音波射頻信號（訓練組資料）決定，不需人為設定，故屬全自動、具可適性的對比增強方法。經仿體實驗驗證，本方法在定量上可抑制組織強散射信號達1.0 dB、增強微氣泡散射信號達11.5 dB，亦即使微氣泡對強散射組織的對比增強達12.5 dB之多，而在定性上也較能顯示出氣泡分布區域。然而由本方法所得之對比增強影像所提供的資訊與傳統B-mode影像不同，不應以傳統方式判讀，因此本研究中亦提出一融合傳統B-mode影像與對比增強影像的方法，同樣為全自動且具可適性，經實驗驗證可同時提供組織型態與微氣泡分布資訊。在本研究的實驗架構下，由本方法所得的對比增強效果遠優於二倍頻影像，且稍較次諧波影像佳，惟訓練組與測試組資料需取自同一影像系統架構。此外，本研究所提出的權重決定方式亦非唯一，仍有其他可能性，但只要是以訓練的方式決定計算參數，對比增強的效果與可靠度必將與訓練組資料量有關。最後，本研究所提出方法的計算量顯著大於傳統非線性成像方法，因此未來工作除了將此方法應用於其他影像系統以及臨床資料外，還需調整計算參數，或改用自經驗模態分解法衍伸出的演算法，使計算量與對比增強效果有較好的平衡。

關鍵字

超音波對比劑影像；微氣泡；非線性成像；經驗模態分解法；總體經驗模態分解法

並列摘要

Ultrasound nonlinear contrast imaging using microbubble-based contrast agents has been widely investigated. However, the degree of contrast enhancement is often limited by overlap between the spectra of the tissue and microbubble nonlinear responses, which makes it difficult to separate them. The use of ensemble empirical mode decomposition (EEMD) in the Hilbert-Huang transform (HHT) was previously explored with the aim of alleviating this problem. The HHT is designed for analyzing nonlinear and nonstationary data, whereas EEMD is a method associated with the HHT that allows decomposition of data into a finite number of intrinsic mode functions (IMFs). It was found that the contrast can be effectively improved in certain IMFs, but manual selection of appropriate IMFs is still required. This prompted the present study to test the hypothesis that the contrast can be enhanced without requiring manual selection by summing appropriately weighted IMFs and demodulating the signal at appropriate frequencies. That is, a data-driven mechanism for automatically determining weights and demodulation frequencies was derived and tested. Users only have to specify the microbubble distribution in the training data set, and the contrasts in testing data sets can be improved. Phantom results show that an overall contrast enhancement of up to 12.5 dB can be achieved. A fused-image representation that simultaneously displays the conventional B-mode image and the new contrast mode image is also presented. The proposed method outperforms second-harmonic imaging significantly, but is only slightly better than subharmonic imaging on experimental data. However, there is a limitation that the imaging setups should be identical for obtaining training and testing data. Though there are other means to determine the weights, as long as they are determined through a training process, the contrast improvement and the reliability of the results will mainly depend on the size of the training data set. Finally, in general the proposed method demands more computations than conventional methods. Hence, future studies will not only tempt to apply the method to other imaging configurations and clinical data, but also seek for a set of computational parameters or utilize other algorithms derived from ensemble empirical decomposition (EMD) to better balance computational complexity and contrast improvement.

並列關鍵字

ultrasound contrast imaging ； microbubble ； nonlinear imaging ； empirical mode decomposition (EMD) ； ensemble empirical mode decomposition (EEMD)

參考文獻

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使用總體經驗模態分解法自動增強超音波影像對比

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