ArticleJournal of biomedical optics2018
Back-propagation neural network-based reconstruction algorithm for diffuse optical tomography.
Article in Journal of biomedical optics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Football players' strength training method using image processing based on machine learning.PloS one · 2023Trial
- Machine-learning-enhanced image reconstruction in optical tomography using the Monte Carlo method for light transport.Journal of biomedical optics · 2026Article
- Deep Learning Enables Fast and Accurate Quantification of MRI-Guided Near-Infrared Spectral Tomography for Breast Cancer Diagnosis.IEEE transactions on medical imaging · 2025Article
- Model-Based Convolution Neural Network for 3D Near-Infrared Spectral Tomography.IEEE transactions on medical imaging · 2025Article
- A Multitask CNN for Near-Infrared Probe: Enhanced Real-Time Breast Cancer Imaging.Sensors (Basel, Switzerland) · 2025Article
- Towards real-time diffuse optical tomography with a handheld scanning probe.Biomedical optics express · 2025Article
- Advancing Near-Infrared Probes for Enhanced Breast Cancer Assessment.Sensors (Basel, Switzerland) · 2025Article
- Detection of breast cancer using machine learning on time-series diffuse optical transillumination data.Journal of biomedical optics · 2024Article
- Diffuse optical tomography of the brain: effects of inaccurate baseline optical parameters and refinements using learned post-processing.Biomedical optics express · 2024Article
- Deep learning-enabled high-speed, multi-parameter diffuse optical tomography.Journal of biomedical optics · 2024Article
- Automated pipeline for breast cancer diagnosis using US assisted diffuse optical tomography.Biomedical optics express · 2023Article
- 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 (Bellingham, Wash.) · 2023Article
- FDU-Net: Deep Learning-Based Three-Dimensional Diffuse Optical Image Reconstruction.IEEE transactions on medical imaging · 2023Article
- Two-stage classification strategy for breast cancer diagnosis using ultrasound-guided diffuse optical tomography and deep learning.Journal of biomedical optics · 2023Article
- An Optical Sensory System for Assessment of Residual Cancer Burden in Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy.Sensors (Basel, Switzerland) · 2023Article
- Fusion deep learning approach combining diffuse optical tomography and ultrasound for improving breast cancer classification.Biomedical optics express · 2023Article
- Periodic-net: an end-to-end data driven framework for diffuse optical imaging of breast cancer from noisy boundary data.Journal of biomedical optics · 2023Article
- High-Speed Time-Domain Diffuse Optical Tomography with a Sensitivity Equation-based Neural Network.IEEE transactions on computational imaging · 2023Article
- Sensor-to-Image Based Neural Networks: A Reliable Reconstruction Method for Diffuse Optical Imaging of High-Scattering Media.Sensors (Basel, Switzerland) · 2022Article
- High-density diffuse correlation tomography with enhanced depth localization and minimal surface artefacts.Biomedical optics express · 2022Article
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5 authors.
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Abstract
Diffuse optical tomography (DOT) is a promising noninvasive imaging modality and is capable of providing functional characteristics of biological tissue by quantifying optical parameters. The DOT image reconstruction is ill-posed and ill-conditioned, due to the highly diffusive nature of light propagation in biological tissues and limited boundary measurements. The widely used regularization technique for DOT image reconstruction is Tikhonov regularization, which tends to yield oversmoothed and low-quality images containing severe artifacts. It is necessary to accurately choose a regularization parameter for Tikhonov regularization. To overcome these limitations, we develop a noniterative reconstruction method, whereby optical properties are recovered based on a back-propagation neural network (BPNN). We train the parameters of BPNN before DOT image reconstruction based on a set of training data. DOT image reconstruction is achieved by implementing a single evaluation of the trained network. To demonstrate the performance of the proposed algorithm, we compare with the conventional Tikhonov regularization-based reconstruction method. The experimental results demonstrate that image quality and quantitative accuracy of reconstructed optical properties are significantly improved with the proposed algorithm.
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