Optimizing diffuse optical imaging for breast tissues with a dual-encoder neural network to preserve small structural information and fine features

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

doi:10.1117/1.JMI.10.6.066003

5 December 2023 Optimizing diffuse optical imaging for breast tissues with a dual-encoder neural network to preserve small structural information and fine features

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

Author Affiliations +

Journal of Medical Imaging, Vol. 10, Issue 6, 066003 (December 2023). https://doi.org/10.1117/1.JMI.10.6.066003

Abstract

Purpose

Various laboratory sources have recently achieved progress in implementing deep learning models on biomedical optical imaging of soft biological tissues. The highly scattered nature of tissues at specific optical wavelengths results in poor spatial resolution. This opens up opportunities for diffuse optical imaging to improve the spatial resolution of obtained optical properties suffering from artifacts. This study aims to investigate a dual-encoder deep learning model for successfully detecting tumors in different phantoms w.r.t tumor size on diffuse optical imaging.

Approach

Our proposed dual-encoder network extends U-net by adding a parallel branch of signal data to get information directly from the base source. This allows the trained network to localize the inclusions without degrading or merging with the background. The signals from the forward model and the images from the inverse problem are combined in a single decoder, filling the gap between existing direct processing and post-processing.

Results

Absorption and reduced scattering coefficients are well reconstructed in both simulation and phantom test datasets. The proposed and implemented dual-encoder networks characterize better optical-property images than the signal-encoder and image-encoder networks, and the contrast-and-size detail resolution of the dual-encoder networks outperforms the other two approaches. From the measures of performance evaluation, the structural similarity and peak signal-to-noise ratio of the reconstructed images obtained by the dual-encoder networks remain the highest values.

Conclusions

In this study, we synthesized the advantages of boundary data direct reconstruction, namely the extracted signals and iterative methods, from the obtained images into a unified network architecture.

Citation Download Citation

Nazish Murad, Min-Chun Pan, and Ya-Fen Hsu "Optimizing diffuse optical imaging for breast tissues with a dual-encoder neural network to preserve small structural information and fine features," Journal of Medical Imaging 10(6), 066003 (5 December 2023). https://doi.org/10.1117/1.JMI.10.6.066003

Received: 17 April 2023; Accepted: 17 November 2023; Published: 5 December 2023

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KEYWORDS

Image restoration

Education and training

Optical properties

Deep learning

Breast

Diffuse optical imaging

Tissues

Purpose

Approach

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Conclusions

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