Nair Anuja, Kuban Barry D, Tuzcu E Murat, Schoenhagen Paul, Nissen Steven E, Vince D Geoffrey
Department of Biomedical Engineering, The Cleveland Clinic Foundation, OH 44195, USA.
Circulation. 2002 Oct 22;106(17):2200-6. doi: 10.1161/01.cir.0000035654.18341.5e.
Atherosclerotic plaque stability is related to histological composition. However, current diagnostic tools do not allow adequate in vivo identification and characterization of plaques. Spectral analysis of backscattered intravascular ultrasound (IVUS) data has potential for real-time in vivo plaque classification.
Eighty-eight plaques from 51 left anterior descending coronary arteries were imaged ex vivo at physiological pressure with the use of 30-MHz IVUS transducers. After IVUS imaging, the arteries were pressure-fixed and corresponding histology was collected in matched images. Regions of interest, selected from histology, were 101 fibrous, 56 fibrolipidic, 50 calcified, and 70 calcified-necrotic regions. Classification schemes for model building were computed for autoregressive and classic Fourier spectra by using 75% of the data. The remaining data were used for validation. Autoregressive classification schemes performed better than those from classic Fourier spectra with accuracies of 90.4% for fibrous, 92.8% for fibrolipidic, 90.9% for calcified, and 89.5% for calcified-necrotic regions in the training data set and 79.7%, 81.2%, 92.8%, and 85.5% in the test data, respectively. Tissue maps were reconstructed with the use of accurate predictions of plaque composition from the autoregressive classification scheme.
Coronary plaque composition can be predicted through the use of IVUS radiofrequency data analysis. Autoregressive classification schemes performed better than classic Fourier methods. These techniques allow real-time analysis of IVUS data, enabling in vivo plaque characterization.
动脉粥样硬化斑块稳定性与组织学组成相关。然而，目前的诊断工具无法在体内充分识别和表征斑块。背向散射血管内超声（IVUS）数据的频谱分析具有在体内实时进行斑块分类的潜力。
使用30MHz的IVUS换能器在生理压力下对51支左前降支冠状动脉中的88个斑块进行离体成像。IVUS成像后，对动脉进行压力固定，并在匹配图像中收集相应的组织学样本。从组织学样本中选取的感兴趣区域包括101个纤维区域、56个纤维脂质区域、50个钙化区域和70个钙化坏死区域。通过使用75%的数据计算自回归和经典傅里叶频谱的模型构建分类方案。其余数据用于验证。在训练数据集中，自回归分类方案的表现优于经典傅里叶频谱，纤维区域的准确率为90.4%，纤维脂质区域为92.8%，钙化区域为90.9%，钙化坏死区域为89.5%；在测试数据中，准确率分别为79.7%、81.2%、92.8%和85.5%。利用自回归分类方案对斑块成分的准确预测重建了组织图。
通过IVUS射频数据分析可以预测冠状动脉斑块成分。自回归分类方案比经典傅里叶方法表现更好。这些技术允许对IVUS数据进行实时分析，从而实现体内斑块表征。
