超音波成像雖已廣泛用於評估組織病變之形態學變化，但這些變化通常缺乏病變特異性，導致大多數常規超音波成像模式難以精確診斷組織病變程度。另一方面，組織灌流變化雖可精確反映組織功能受損與復原程度，但常規超音波系統囿於低空間解析度與血管對比度，導致難以對其進行有效評估。空間解析度雖可透過提升超音波頻率增加，但頻率提升亦會不可避免地導致超音波的衰減、穿透深度與影像對比度的降低。為改善上述問題，對比增強超音波成像技術透過超音波對比劑注射增強血管回聲訊號，透過計算回聲訊號強度之時間-強度曲線與時間-Nakagami參數-曲線之灌流參數便可量化組織灌流，但這些技術分別會受到組織雜波與亞解析效應影響而降低評估之可重複性與穩定性。
為克服上述問題，本研究開發基於超音波對比劑非線性與高壓力敏感度等特性之對比特異性超音波成像系統，進一步提升超音波灌流評估之可重複性及穩定性。為決定系統最佳化參數，三個涵蓋3至12 MHz之超音波換能器被安裝於系統，並透過調控0.3至1.2 MPa入射聲壓對超音波對比劑懸浮液進行量測。實驗結果顯示超音波對比劑懸浮液於共振頻率(7 MHz)時隨著入射聲壓從0.3 MPa增加到1.2 MPa，Nakagami參數從0.81 ± 0.03降至0.73 ± 0.02，此結果證實Nakagami參數可反映頻率與壓力變化對超音波對比劑之影響。因此本研究進一步提出調變複合視窗Nakagami參數比值成像技術，此技術透過對超音波對比劑灌流區域交互入射兩個不同聲壓以增強超音波對比劑的非線性特性，得到調變複合視窗Nakagami參數比值影像與時間-Nakagami參數比值曲線及灌流參數。組織仿體與動物驗證結果顯示時間-Nakagami參數比值曲線較常規組織灌流評估技術有更好的組織雜波與亞解析效應耐受度。本研究亦提出調變複合視窗Nakagami參數比值與B-mode融合影像，以結合對比劑特異性與結構性影像優勢。
為進一步探討對比特異性超音波成像系統對組織病變織偵測能力，並降低主觀與系統因素影響，本研究整合定量超音波參數、灌流參數與對比增強超音波影像，以機器及深度學習方法自動評估肌肉損傷。透過對共計12隻大鼠產生腓腸肌挫傷性損傷，並使用超音波對比劑特異性成像系統進行為期三周的測挫傷恢復過程量測，獲得對比增強超音波影像及參數。並使用腓腸肌損傷等級對肌肉恢復階段進行分類，等級0、1與2分別對應至健康(未挫傷組織)、破壞與修復與重塑階段。接著以定量超音波參數及灌流參數結合三種常規機器學習方法，包括貝氏分類器、支持向量機、人工神經網絡對腓腸肌損傷等級進行分類，其分類準確度分別為57.7 ± 8.54、62.22 ± 1.37與66.00 ± 1.39。本研究亦採用AlexNet、GoogleNet與VGG-19等深度卷積神經網路自動擷取對比增強超音波影像之特徵對腓腸肌損傷等級進行分類，其分類準確度分別為95.31 ± 0.33%、99.36 ± 0.32%與99.63 ± 0.35%。實驗結果顯示調變複合視窗Nakagami參數比值影像與時間-Nakagami參數比值曲線較不易受到肌肉纖維損傷與疤痕組織雜波影響，並証實結合深度學習方法與對比增強超音波影像可準確地分類肌肉損傷復原程度。

Ultrasound imaging has been widely used to assess the morphological changes of tissue lesions, however, morphological related changes usually lack of disease specificity. Consequently, it remains difficult for the majority of ultrasound B-mode imaging to precisely diagnose a specific tissue lesion, since the morphological changes of tissues are usually non-disease-specific. On the other hand, microvascular changes can accurately reflect the severity and recovery of tissue lesions. To further alleviate these hurdles, the administrated ultrasound contrast agents (UCAs) in the bloodstream allow the acquisition of contrast-enhanced ultrasound (CEUS) imaging over a certain duration, to estimate the tissue perfusion. Nevertheless, the frequency of most of the diagnostic ultrasounds is less than 10 MHz, and that tends to result in a short-of-sufficient spatial resolution, sampling rate, tissue clutter tolerance to measure the blood flow or perfusion in the capillary beds. Certainly, it is straightforward to increase the resolution of the ultrasound image by the increase of employed ultrasound frequency. Nevertheless, the increase of ultrasound frequency tends to unavoidably increase the acoustic attenuation and then decrease the depth and contrast of the image greatly. Therefore, it certainly is desirable to further explore and develop alternate diagnostics for better assessing the states of lesions or treatment effect covering the microvascular changes in local tissue.
To further alleviate these issues, the present study developed a contrast-specific ultrasound imaging system using the non-linear and pressure-dependence characteristics of UCAs. To explore the effects of backscattering properties of UCAs, the contrast-specific ultrasound imaging system was equipped with three different ultrasonic transducers, which were developed for measuring the UCAs suspensions. Various pressure amplitudes of transmitted ultrasound ranging from 0.3 to 1.2 MPa corresponding to each ultrasound frequency, were also adjusted. The tendency of the Nakagami parameter as a function of ultrasound frequency was opposite to that of backscattered power and that the Nakagami parameter of UCAs decreased from 0.92 ± 0.05 to 0.80 ± 0.03 as the driving frequencies increase from 3 to 7 MHz. As the UCAs suspensions were insonified at 7 MHz, the Nakagami parameter was dramatically decreased from 0.81 ± 0.03 to 0.73 ± 0.02 with the increase of incident acoustic pressure from 0.3 to 1.2 MPa. These results indicate the Nakagami parameter can effectively reflect the frequency and pressure changes on UCAs. Specifically, the perfusion parameters estimated from the ultrasound time-intensity curve (TIC) and statistics-based time-Nakagami parameter curve (TNC) approaches were found able to quantify the perfusion. Nevertheless, due to insufficient tolerance on tissue clutters and subresolvable effects, Nakagami parameter-based approaches remain short of reproducibility and stability. Therefore, in this study, the window-modulated compounding (WMC) Nakagami parameter ratio imaging was proposed to alleviate these effects, by taking the ratio of WMC Nakagami parameters corresponding to the incidence of two different acoustic pressures from an employed transducer. The time-Nakagami parameter ratio curve (TNRC) approach was also developed to estimate perfusion parameters. The verification of contrast-specific system and WMC Nakagami parameter ratio approach were performed from flow phantom and animal subjects administrated with a bolus of UCAs. The TNRC approach demonstrated better sensitivity and tolerance of tissue clutters than those of TIC and TNC. The fusion image with the WMC Nakagami parameter ratio and B-mode images indicated that both the tissue structures and perfusion properties of ultrasound contrast agents may be better discerned.
To extensively explore the contrast-specific ultrasound imaging system for noninvasively imaging and perfusion evaluation of tissue lesions, and alleviate the effects of subjective and system factors. In the present study, efforts were made aiming to further improve the detection and classification of muscle injury with ultrasound compound imaging that fused quantitative ultrasound and perfusion parameters. Animal experiments were performed from a total of 12 rats, where the contusion injury in response to a certain impact was made on their gastrocnemius muscle. Each measurement was carried out using the contrast-specific ultrasound imaging system that covered the recovery phases of contusion for three weeks to obtain CEUS images and those just mentioned parameters. The muscle recovery phases were classified by designating the gastrocnemius muscle injury level (GIL) of 0 to the healthy stage that corresponds to the uncontused tissue; those of 1 and 2 to the destruction and repair and remodeling phases associated with the certain muscle recovery phases. Subsequently, three conventional machine learning approaches, including naive Bayes (NB), support vector machine (SVM), and artificial neural network (ANN) were employed to classify GILs utilizing quantitative ultrasound and perfusion parameters. Moreover, AlexNet, GoogleNet, and VGG-19 were adopted for training to extract the feature maps of those acquired CEUS images for automatically classifying the GIL with a deep convolutional neural network. The classification with NB, SVM, ANN, AlexNet, GoogleNet, and VGG-19 resulted in accuracies of 57.7 ± 8.54, 62.22 ± 1.37, 66.00 ± 1.39, 95.31 ± 0.33%, 99.36 ± 0.32%, and 99.63 ± 0.35%. These results indicated that the CEUS in conjunction with WMC Nakagami parameter ratio imaging has the ability for assessing tissue lesions. Moreover, the results also demonstrate that TNRC is able to reduce the effect of tissue clutters from injured muscle fibers and scar tissues thus increase the reproducibility for assessing microcirculation. In addition, this suggests deep learning approaches combined with the CEUS images are of potential to sensitively detect and classify the muscle injury.

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