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Related Experiment Video

Updated: May 12, 2026

Fabrication and Characterization of Optical Tissue Phantoms Containing Macrostructure

Fabrication and Characterization of Optical Tissue Phantoms Containing Macrostructure

Published on: February 12, 2018

Accurate attenuation characterization in optical coherence tomography using multi-reference phantoms and deep

Nian Peng¹, Chengli Xu¹, Yi Shen²

¹School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China.

Biomedical Optics Express

|December 16, 2024

Summary

This summary is machine-generated.

Accurately measuring light attenuation in tissues using optical coherence tomography (OCT) is challenging. A new deep learning method, MR-Net, significantly improves the accuracy of calculating the optical attenuation coefficient (AC) from OCT signals.

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Last Updated: May 12, 2026

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Area of Science:

Biomedical Optics
Medical Imaging
Optical Coherence Tomography

Background:

The optical attenuation coefficient (AC) is vital for quantitative tissue analysis and differentiation.
Accurate AC quantification from optical coherence tomography (OCT) signals is a significant challenge in clinical applications.

Purpose of the Study:

To investigate factors affecting AC extraction accuracy in existing OCT algorithms.
To develop a novel deep learning approach (MR-Net) for improved AC quantification from OCT data.

Main Methods:

A Multi-Reference Phantom Driven Network (MR-Net) was developed using deep learning and multi-reference phantoms.
Intralipid and silicone-TiO2 phantoms with known AC values were imaged using a 1300 nm swept-source OCT system.
Performance was compared against existing OCT-based AC extraction algorithms, analyzing data length, out-of-focus distance, and phantom properties.

Main Results:

MR-Net demonstrated superior performance across all evaluated metrics.
MR-Net achieved an average relative error of 10.43% for AC calculation, significantly outperforming existing methods (lowest 23.72%).
The network effectively models complex physical effects influencing OCT signal propagation.

Conclusions:

MR-Net offers a highly accurate and automated method for quantifying the optical attenuation coefficient from OCT signals.
This approach provides a robust quantitative framework for disease diagnosis and enhanced tissue characterization in clinical settings.