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Multimodal Imaging and Spectroscopy Fiber-bundle Microendoscopy Platform for Non-invasive, In Vivo Tissue Analysis

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Learning to image and compute with multimode optical fibers.

Babak Rahmani¹, Ilker Oguz^1,2, Ugur Tegin^1,2

¹Laboratory of Applied Photonics Devices, School of Engineering, Ecole Polytechnique Fédérale de Lausanne, Institute of Electrical and MicroEngineering, Lausanne, 1015, Switzerland.

Nanophotonics (Berlin, Germany)

|December 5, 2024

Summary

This summary is machine-generated.

This paper reviews novel machine learning approaches for image transmission through multimode fibers (MMF), potentially revolutionizing medical endoscopy by enabling thinner, high-resolution imaging devices. It discusses methods like phase conjugation and the transmission matrix for advanced optical imaging.

Keywords:

deep neural network imaging multimode fibers neuromorphic computing

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Area of Science:

Optics and Photonics
Medical Imaging
Machine Learning

Background:

Multimode fibers (MMF) were initially for digital data but show potential for analog image transmission.
Current endoscopes use thick fiber bundles; MMFs could enable thinner, less invasive devices for high-resolution imaging.
Machine learning (ML) offers novel ways to image and transmit data through MMFs.

Purpose of the Study:

To review novel machine learning (ML) approaches for imaging and image transmission via multimode fibers (MMF).
To explore the application of MMFs in performing ML tasks.
To discuss the advantages and disadvantages of ML-based imaging compared to conventional methods.

Main Methods:

Review of techniques for imaging in scattering media, specifically MMFs, involving phase and amplitude measurement.
Discussion of analog phase conjugation, digital phase conjugation, and the full-wave holographic transmission matrix method.
Exploration of iterative optimization techniques for image tasks like focusing and display through MMFs.

Main Results:

MMFs can transmit multiple spatial modes, allowing for potential replacement of current thick fiber bundles in endoscopes with a single, thin fiber.
ML approaches, combined with techniques like phase conjugation and transmission matrix methods, enable high-resolution imaging through MMFs.
The transmission matrix method is highlighted as a gold standard for reconstructing input-output relationships in MMFs.

Conclusions:

MMF-based imaging with ML has the potential to revolutionize medical endoscopy, enabling less invasive procedures and access to previously unreachable tissues.
ML offers powerful tools for overcoming the complexities of light propagation in MMFs for advanced imaging applications.
Further research into ML and MMFs can lead to significant advancements in optical imaging and endoscopic technologies.