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Single-ended recovery of optical fiber transmission matrices using neural networks

Medicine and Health

Single-ended recovery of optical fiber transmission matrices using neural networks

Y. Zheng, T. Wright, et al.

Discover groundbreaking advancements in ultra-thin multimode optical fiber imaging, revolutionizing medical endoscopes with high-resolution imaging capabilities. Authors Yijie Zheng, Terry Wright, Zhong Wen, Qing Yang, and George S. D. Gordon present a neural network-based method to address optical distortion issues, enabling rapid and robust image reconstruction.

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Playback language: English
Abstract
Ultra-thin multimode optical fiber imaging holds promise for next-generation medical endoscopes, offering high-resolution images of deep tissues. However, current technology suffers from severe optical distortion due to fiber sensitivity to bending and temperature, requiring in vivo recalibration with single-ended access. This paper introduces a neural network (NN)-based approach to reconstruct the fiber's transmission matrix (TM) using multi-wavelength reflection-mode measurements. Two NN architectures—a fully connected NN and a convolutional U-Net—are trained using a custom loss function insensitive to global phase degeneracy. The method reconstructs 64 x 64 complex-valued fiber TMs with ≤4% error in simulations and validates results on experimental TMs. The approach enables fast, robust wide-field and confocal image reconstruction, and it is 4500 times faster and more robust to fiber perturbations than previous iterative methods. The method is also adaptable to non-square TMs.
Publisher
Communications Physics
Published On
Oct 18, 2023
Authors
Yijie Zheng, Terry Wright, Zhong Wen, Qing Yang, George S. D. Gordon
Tags
optical fiber imaging
medical endoscopes
neural network
transmission matrix
image reconstruction
optical distortion
multi-wavelength measurements
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