CONFERENCE PROCEEDINGS
Advanced Search
Home > Journals > Neurophoton. > Volume 11 > Issue S1 > Article
Open Access
31 January 2024 Resolution-enhanced multi-core fiber imaging learned on a digital twin for cancer diagnosis
Tijue Wang, Jakob Dremel, Sven Richter, Witold Polanski, Ortrud Uckermann, Ilker Eyüpoglu, Jürgen W. Czarske, Robert Kuschmierz
Author Affiliations +
Neurophotonics, Vol. 11, Issue S1, S11505 (January 2024). https://doi.org/10.1117/1.NPh.11.S1.S11505
Abstract

Significance

Deep learning enables label-free all-optical biopsies and automated tissue classification. Endoscopic systems provide intraoperative diagnostics to deep tissue and speed up treatment without harmful tissue removal. However, conventional multi-core fiber (MCF) endoscopes suffer from low resolution and artifacts, which hinder tumor diagnostics.

Aim

We introduce a method to enable unpixelated, high-resolution tumor imaging through a given MCF with a diameter of around 0.65 mm and arbitrary core arrangement and inhomogeneous transmissivity.

Approach

Image reconstruction is based on deep learning and the digital twin concept of the single-reference-based simulation with inhomogeneous optical properties of MCF and transfer learning on a small experimental dataset of biological tissue. The reference provided physical information about the MCF during the training processes.

Results

For the simulated data, hallucination caused by the MCF inhomogeneity was eliminated, and the averaged peak signal-to-noise ratio and structural similarity were increased from 11.2 dB and 0.20 to 23.4 dB and 0.74, respectively. By transfer learning, the metrics of independent test images experimentally acquired on glioblastoma tissue ex vivo can reach up to 31.6 dB and 0.97 with 14 fps computing speed.

Conclusions

With the proposed approach, a single reference image was required in the pre-training stage and laborious acquisition of training data was bypassed. Validation on glioblastoma cryosections with transfer learning on only 50 image pairs showed the capability for high-resolution deep tissue retrieval and high clinical feasibility.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Citation Download Citation
Tijue Wang, Jakob Dremel, Sven Richter, Witold Polanski, Ortrud Uckermann, Ilker Eyüpoglu, Jürgen W. Czarske, and Robert Kuschmierz "Resolution-enhanced multi-core fiber imaging learned on a digital twin for cancer diagnosis," Neurophotonics 11(S1), S11505 (31 January 2024). https://doi.org/10.1117/1.NPh.11.S1.S11505
Received: 20 September 2023; Accepted: 8 January 2024; Published: 31 January 2024
JOURNAL ARTICLE
 7 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
KEYWORDS
Multicore fiber
Image retrieval
Tissues
Biological imaging
Inhomogeneities
Education and training
Image restoration
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top