National Central University, Department of Mechanical Engineering, Taoyuan City, Taiwan.
Landseed Hospital International, Department of Surgery, Taoyuan City, Taiwan.
J Biomed Opt. 2023 Feb;28(2):026001. doi: 10.1117/1.JBO.28.2.026001. Epub 2023 Feb 6.
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.
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.
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 , , and boundary measurement setups.
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.
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.
机器学习（ML）方法在评估生物医学成像过程中起着至关重要的作用，特别是光学成像（OI），包括分割、分类和重建，旨在有效地提高准确性。
本研究旨在开发一个具有多个数据集的端到端深度学习框架，用于检测乳腺癌并在早期重建其光学特性。
所提出的周期网络是一种无损深度学习（DL）算法，用于在具有高精度的逆模型中重建和评估非均匀性，而边界测量则通过在不同组合下在圆形域周围均匀排列的源/探测器求解正问题来计算，包括 、 和 边界测量设置。
数值和体模数据集上的图像重建结果表明，与其他最先进的竞争对手相比，所提出的网络能够提供更高质量的图像，具有更多的小细节，对噪声的免疫力更强，边缘更锐利，图像伪影更少。
该网络通过优化成像时间，在不降低包含物定位和图像质量的情况下，非常有效地同时重建光学性质，即吸收和减少散射系数。
