ArticleBiomedical optics express2021
Machine learning model with physical constraints for diffuse optical tomography.
Article in Biomedical optics express, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Sensing Through Tissues Using Diffuse Optical Imaging and Genetic Programming.Sensors (Basel, Switzerland) · 2026Article
- Ultrasound-guided diffuse optical tomography: an adjunct to ultrasound that can reduce unnecessary breast biopsies.Breast cancer research : BCR · 2025Article
- Deep Learning Enables Fast and Accurate Quantification of MRI-Guided Near-Infrared Spectral Tomography for Breast Cancer Diagnosis.IEEE transactions on medical imaging · 2025Article
- DOT-AE-GAN: a hybrid autoencoder-GAN model for enhanced ultrasound-guided diffuse optical tomography reconstruction.Journal of biomedical optics · 2025Article
- Model-Based Convolution Neural Network for 3D Near-Infrared Spectral Tomography.IEEE transactions on medical imaging · 2025Article
- Towards real-time diffuse optical tomography with a handheld scanning probe.Biomedical optics express · 2025Article
- Improving diffuse optical tomography imaging quality using APU-Net: an attention-based physical U-Net model.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
- Enhanced model iteration algorithm with graph neural network for diffuse optical tomography.Biomedical optics express · 2024Article
- Roadmap on computational methods in optical imaging and holography [invited].Applied physics. B, Lasers and optics · 2024Review
- Automated pipeline for breast cancer diagnosis using US assisted diffuse optical tomography.Biomedical optics express · 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
- Machine learning to optimize additive manufacturing for visible photonics.Nanophotonics (Berlin, Germany) · 2023Article
- Fusion deep learning approach combining diffuse optical tomography and ultrasound for improving breast cancer classification.Biomedical optics express · 2023Article
- Machine Learning Diffuse Optical Tomography Using Extreme Gradient Boosting and Genetic Programming.Bioengineering (Basel, Switzerland) · 2023Article
- Deep learning-based fusion of widefield diffuse optical tomography and micro-CT structural priors for accurate 3D reconstructions.Biomedical optics express · 2023Article
- Unrolled-DOT: an interpretable deep network for diffuse optical tomography.Journal of biomedical optics · 2023Article
- Selfrec-Net: self-supervised deep learning approach for the reconstruction of Cherenkov-excited luminescence scanned tomography.Biomedical optics express · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
A machine learning model with physical constraints (ML-PC) is introduced to perform diffuse optical tomography (DOT) reconstruction. DOT reconstruction is an ill-posed and under-determined problem, and its quality suffers by model mismatches, complex boundary conditions, tissue-probe contact, noise etc. Here, for the first time, we combine ultrasound-guided DOT with ML to facilitate DOT reconstruction. Our method has two key components: (i) a neural network based on auto-encoder is adopted for DOT reconstruction, and (ii) physical constraints are implemented to achieve accurate reconstruction. Both qualitative and quantitative results demonstrate that the accuracy of the proposed method surpasses that of existing models. In a phantom study, compared with the Born conjugate gradient descent (Born-CGD) reconstruction method, the ML-PC method decreases the mean percentage error of the reconstructed maximum absorption coefficient from 16.41% to 13.4% for high contrast phantoms and from 23.42% to 9.06% for low contrast phantoms, with improved depth distribution of the target absorption maps. In a clinical study, better contrast was obtained between malignant and benign breast lesions, with the ratio of the medians of the maximum absorption coefficient improved from 1.63 to 2.22.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.