Noritomi T, Sigel B, Gahtan V, Swami V, Justin J, Feleppa E, Shirouzu K
Department of Surgery, Allegheny University of the Health Sciences, Philadelphia, Pennsylvania 19129, USA.
J Ultrasound Med. 1997 Feb;16(2):107-11. doi: 10.7863/jum.1997.16.2.107.
The purpose of the study was to determine whether ultrasonic tissue characterization could detect carotid plaque thrombus in vivo. Patients undergoing carotid endarterectomy were examined preoperatively and the ultrasonic tissue characterization findings were compared to those of optical microscopy of the removed plaque specimens. Ten of 15 patients studied had plaque thrombus. Ultra-ultrasonic tissue characterization entailed an analysis of parameters obtained from the power spectrum of backscattered ultrasound signals. Data were obtained with a nominal 10 MHz sector scanning transducer with an effective bandwidth of 3 to 13 MHz. The parameters were the slope and intercept derived from the linear regression of the normalized spectrum and total power (log of the integrated power of the normalized spectrum over the effective bandwidth). The combined effect of the three parameters was determined by discriminant function analysis and showed a significant difference (P < 0.05) between nonthrombus and plaque thrombus in a small sample of patients with advance carotid atherosclerosis. These parameters applied singly could not provide such a distinction. Correct classification of carotid plaque thrombus using the multiple-parameter analysis revealed a sensitivity of 90%, specificity of 80%, and accuracy of 86.7%. This study demonstrates that analysis utilizing a combination of multiple spectral parameters was able to detect carotid plaque thrombus in vivo.
本研究的目的是确定超声组织特征分析能否在体内检测出颈动脉斑块内的血栓。对接受颈动脉内膜切除术的患者进行术前检查，并将超声组织特征分析结果与切除的斑块标本的光学显微镜检查结果进行比较。在研究的15例患者中，有10例存在斑块内血栓。超声组织特征分析需要对从背向散射超声信号的功率谱中获得的参数进行分析。数据是使用标称频率为10MHz的扇形扫描换能器获得的，其有效带宽为3至13MHz。这些参数是从归一化频谱与总功率（归一化频谱在有效带宽上的积分功率的对数）的线性回归中得出的斜率和截距。通过判别函数分析确定这三个参数的综合效应，结果显示在一小部分患有晚期颈动脉粥样硬化的患者中，无血栓斑块和有血栓斑块之间存在显著差异（P<0.05）。单独应用这些参数无法提供这种区分。使用多参数分析对颈动脉斑块内血栓进行正确分类，其敏感性为90%，特异性为80%，准确性为86.7%。本研究表明，利用多个频谱参数的组合进行分析能够在体内检测出颈动脉斑块内的血栓。
