Peng Nian, Xu Chengli, Shen Yi, Yuan Wu, Yang Xiaoyu, Qi Changhai, Qiu Haixia, Gu Ying, Chen Defu
School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China.
Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou 350117, China.
Biomed Opt Express. 2024 Nov 6;15(12):6697-6714. doi: 10.1364/BOE.543606. eCollection 2024 Dec 1.
The optical attenuation coefficient (AC), a crucial tissue parameter indicating the rate of light attenuation within a medium, enables quantitative analysis of tissue properties and facilitates tissue differentiation. Despite its growing clinical significance, accurate quantification of AC from optical coherence tomography (OCT) signals remains a pressing concern. This study comprehensively investigates the factors influencing the accuracy of quantitative AC extraction among existing OCT-based AC extraction algorithms. Subsequently, we propose an approach, the Multi-Reference Phantom Driven Network (MR-Net), which leverages multi-reference phantoms and deep learning to implicitly model factors affecting OCT signal propagation, thereby automatically regressing AC. Using a dataset from Intralipid and silicone-TiO phantoms with known AC values obtained from a collimated transmission system and imaged with a 1300 nm swept-source OCT system, we conducted a thorough comparison focusing on data length, out-of-focus distance, and reference phantoms' attenuation among existing OCT-based AC extraction algorithms. By leveraging this extensive dataset, MR-Net can automatically model the complex physical effects in the transmission process of OCT signals, significantly enhancing the accuracy of AC predictions. MR-Net outperforms other algorithms in all metrics, achieving an average relative error of only 10.43% for calculating attenuation samples, significantly lower than the lowest value of 23.72% achieved by other algorithms. This method offers a quantitative framework for disease diagnosis, ultimately contributing to more accurate and effective tissue characterization in clinical settings.
光学衰减系数（AC）是一个关键的组织参数，它表明了光在介质中的衰减速率，能够对组织特性进行定量分析并有助于组织鉴别。尽管其临床意义日益凸显，但从光学相干断层扫描（OCT）信号中准确量化AC仍然是一个紧迫的问题。本研究全面调查了现有基于OCT的AC提取算法中影响定量AC提取准确性的因素。随后，我们提出了一种方法，即多参考体模驱动网络（MR-Net），该方法利用多参考体模和深度学习来隐式建模影响OCT信号传播的因素，从而自动回归AC。我们使用了来自Intralipid和硅胶-TiO体模的数据集，这些体模具有通过准直传输系统获得的已知AC值，并使用1300 nm扫频源OCT系统进行成像，我们对现有基于OCT的AC提取算法的数据长度、离焦距离和参考体模的衰减进行了全面比较。通过利用这个广泛的数据集，MR-Net可以自动对OCT信号传输过程中的复杂物理效应进行建模，显著提高AC预测的准确性。MR-Net在所有指标上均优于其他算法，计算衰减样本的平均相对误差仅为10.43%，明显低于其他算法所达到的最低值23.72%。该方法为疾病诊断提供了一个定量框架，最终有助于在临床环境中更准确有效地进行组织表征。
