IEEE J Biomed Health Inform. 2023 Aug;27(8):3958-3969. doi: 10.1109/JBHI.2023.3276422. Epub 2023 Aug 7.
Intravascular optical coherence tomography (IVOCT) provides high-resolution, depth-resolved images of coronary arterial microstructure by acquiring backscattered light. Quantitative attenuation imaging is important for accurate characterization of tissue components and identification of vulnerable plaques. In this work, we proposed a deep learning method for IVOCT attenuation imaging based on the multiple scattering model of light transport. A physics-informed deep network named Quantitative OCT Network (QOCT-Net) was designed to recover pixel-level optical attenuation coefficients directly from standard IVOCT B-scan images. The network was trained and tested on simulation and in vivo datasets. Results showed superior attenuation coefficient estimates both visually and based on quantitative image metrics. The structural similarity, energy error depth and peak signal-to-noise ratio are improved by at least 7%, 5% and 12.4%, respectively, compared with the state-of-the-art non-learning methods. This method potentially enables high-precision quantitative imaging for tissue characterization and vulnerable plaque identification.
血管内光学相干断层成像（IVOCT）通过获取背向散射光提供冠状动脉微观结构的高分辨率、深度分辨图像。定量衰减成像是准确描述组织成分和识别易损斑块的重要手段。在这项工作中，我们提出了一种基于光传输多重散射模型的 IVOCT 衰减成像深度学习方法。设计了一种物理信息深度学习网络，命名为定量 OCT 网络（QOCT-Net），可以直接从标准 IVOCT B 扫描图像中恢复像素级的光学衰减系数。该网络在模拟和体内数据集上进行了训练和测试。结果表明，与最先进的非学习方法相比，在视觉和基于定量图像指标方面，衰减系数的估计都有明显的改善。结构相似性、能量误差深度和峰值信噪比分别提高了至少 7%、5%和 12.4%。该方法有望实现用于组织特征描述和易损斑块识别的高精度定量成像。
