Periodic-net: an end-to-end data driven framework for diffuse optical imaging of breast cancer from noisy boundary data

Nazish Murad; Min-Chun Pan; Ya-Fen Hsu

doi:10.1117/1.JBO.28.2.026001

6 February 2023 Periodic-net: an end-to-end data driven framework for diffuse optical imaging of breast cancer from noisy boundary data

Nazish Murad, Min-Chun Pan, Ya-Fen Hsu

Author Affiliations +

Journal of Biomedical Optics, Vol. 28, Issue 2, 026001 (February 2023). https://doi.org/10.1117/1.JBO.28.2.026001

Abstract

Significance

The machine learning (ML) approach plays a critical role in assessing biomedical imaging processes especially optical imaging (OI) including segmentation, classification, and reconstruction, intending to achieve higher accuracy efficiently.

Aim

This research aims to develop an end-to-end deep learning framework for diffuse optical imaging (DOI) with multiple datasets to detect breast cancer and reconstruct its optical properties in the early stages.

Approach

The proposed Periodic-net is a nondestructive deep learning (DL) algorithm for the reconstruction and evaluation of inhomogeneities in an inverse model with high accuracy, while boundary measurements are calculated by solving a forward problem with sources/detectors arranged uniformly around a circular domain in various combinations, including 16 × 15, 20 × 19, and 36 × 35 boundary measurement setups.

Results

The results of image reconstruction on numerical and phantom datasets demonstrate that the proposed network provides higher-quality images with a greater amount of small details, superior immunity to noise, and sharper edges with a reduction in image artifacts than other state-of-the-art competitors.

Conclusions

The network is highly effective at the simultaneous reconstruction of optical properties, i.e., absorption and reduced scattering coefficients, by optimizing the imaging time without degrading inclusions localization and image quality.

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

Nazish Murad, Min-Chun Pan, and Ya-Fen Hsu "Periodic-net: an end-to-end data driven framework for diffuse optical imaging of breast cancer from noisy boundary data," Journal of Biomedical Optics 28(2), 026001 (6 February 2023). https://doi.org/10.1117/1.JBO.28.2.026001

Received: 28 October 2022; Accepted: 17 January 2023; Published: 6 February 2023

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