超音波成像與彈性影像已成為許多臨床檢測中不可或缺的工具。然而，這些方法的診斷準確性受到醫師經驗等超音波固有局限的影響，因此許多研究致力於定量與自動化評估以消弭超音波影像解讀的不確定性。其中，定量超音波（quantitative ultrasound, QUS）與人工智慧（artificial intelligent, AI）在提升診斷表現上已取得豐碩成果。因此，自動提取QUS並結合AI進行診斷的技術被認為可提高超音波檢測的效率。然而，結合定量評估與AI應用在連續監測與反應生理現象的可行性仍有待進一步探討。
為解決這些問題，動物活體實驗中使用30 MHz高頻超音波系統擷取病變軟組織的訊號。並透過缺血性皮瓣、傷口癒合與肌肉挫傷模型研究軟組織特性化、組織學分析關聯性、自動分類病理進程與改善彈性影像量測效能的可行性。最後根據實驗之結果評估QUS參數與AI模型在連續監測的表現。
結果顯示第0-7天，皮瓣的嚴重壞死將導致厚度減至67.01%與積體逆散射從-65.74 dB增至-60.66 dB，是由於缺血性壞死進程中的發炎、膠原蛋白萎縮與變質所造成。而第0-14天的傷口癒合過程造成m參數從0.51增至0.85、剪切波速度從5.74 m/s減至2.72 m/s。組織切片中則發現免疫細胞聚集和尚未癒合的傷口，說明參數的變化可對應受損皮膚組織的發炎與受破壞的結構。在挫傷肌肉恢復的評估中，積體逆散射、m參數、剪切波速度、剪切波衰減與紋理特徵隨發炎反應與膠原蛋白再生，在第1-2天達到最大或最小值，並在第3天到21天呈現相反的趨勢。這些實驗中皆證實QUS參數可反映不同生理狀況，並更全面地特徵化軟組織病變的進程。而使用Relief演算法與light gradient boosting machine在肌肉恢復的分類上達到93.57% 的準確度，證實可透過機器學習整合這些參數，以更好地進行軟組織診斷。此外，研究中根據超音波射頻訊號之特性，提出correlation and attention modulated gated recurrent unit (CAM-GRU)網路預測彈性影像的位移量，以改善機械性質量測。結果顯示透過提供相關性資料與注意力機制可顯著增加CAM-GRU於位移量預測的表現，相較傳統方法可達到0.80的皮爾森相關係數。而動物實驗說明在評估挫傷恢復進程與傳統方法有一致的結果，證明CAM-GRU量測彈性的可行性。
本論文中的實驗結果皆證實QUS參數可用於連續評估、監測並對應組織學分析結果。並且，由於個別參數僅反映部分病理變化，AI方法可全面地整合不同定量量測方法，以提升診斷軟組織病變的效能。

Ultrasound imaging and elastography have been an essential modality for a large number of clinical examinations. Yet, the diagnostic accuracy of those methods is compromised by the intrinsic limitation of physician’s experience, and extensive efforts have been made for quantitative and automatic assessment to alleviate the uncertainty in ultrasound interpretation. Particularly, promising results have been proposed to improve diagnosis performance by quantitative ultrasound (QUS) and artificial intelligent (AI). Therefore, techniques combining with automatic QUS extraction and AI applications for diagnosis are proposed able to improve the efficiency of ultrasound examinations. However, further exploration in the feasibility of combining quantitative measurements with AI for continuous monitoring and correlation with physiological conditions is essential.
To address these issues, in vivo animal experiments were implemented to acquire the signals from soft tissue lesions by a 30 MHz high-frequency ultrasound system. The characterization of soft tissue, correlation with histological analysis, automatic classification of pathological progression, and improvement of elastography examination were explored by ischemic skin flap, wound healing, and muscle contusion models. Consequently, the performance of continuous monitoring using QUS parameters and AI models was assessed in accordance with the experimental results.
Results demonstrated that severe necrosis of skin flap led to a decreased thickness to 67.01% and an increased integrated backscatter from -65.74 dB to -60.66 dB from day 0 to 7, corresponding to inflammation, collagen shrinkage and denaturation during the progression of ischemic necrosis. The wound healing progression resulted in an increased m parameter from 0.51 to 0.85 and a decreased shear wave velocity from 5.74 m/s to 2.72 m/s from day 0 to 14. The aggregation of immune cells and unrecovered wounds were found in the histological sections, indicating the parameter variations were associated with inflammation of injured skin tissue and disrupted structure. In the assessment of contused muscle recovery, integrated backscatter, m parameter, shear wave velocity, attenuation, and textural features also achieved the maximum or minimum on day 1 or 2, followed by the reversed tendency from day 3 to 21 in accordance with inflammation and collagen regeneration. These experimental results constantly demonstrated QUS parameters were able to correlate with various physiological conditions and comprehensively characterize the progression of soft tissue lesions. The accuracy of contusion recovery classification was 93.57% by Relief algorithm and light gradient boosting machine, demonstrating the machine learning can integrate these parameters for better diagnosis of soft tissues. Moreover, to improve the measurement of mechanical properties, the correlation and attention modulated gated recurrent unit (CAM-GRU) was proposed based on the characteristics of ultrasound radio-frequency signals for displacement estimation in elastography. Results showed the displacement estimation was improved using CAM-GRU by providing correlation information and attention mechanism, achieving 0.80 Pearson’s correlation coefficient in comparison with conventional method. Furthermore, the results in animal experiments were consistent with the conventional method in assessment of contusion recovery progression, demonstrating the feasibility of measuring elasticity by CAM-GRU.
The experiments in this dissertation all demonstrated that QUS parameters were feasible for continuous monitoring and correlation with histological analysis. Moreover, individual QUS parameter only corresponded to partial physiological conditions. The results of different quantitative measurements can be comprehensively integrated by AI methods to improve the performance of soft tissue lesion diagnosis.

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