ArticleJournal of biomedical optics2024
Deep learning-enabled high-speed, multi-parameter diffuse optical tomography.
Article in Journal of biomedical optics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Machine-learning-enhanced image reconstruction in optical tomography using the Monte Carlo method for light transport.Journal of biomedical optics · 2026Article
- Development of a neural network predicting signals for time-domain diffuse optical tomography.Biomedical engineering letters · 2026Article
- Multi-layer self-calibrated algorithm for transabdominal fetal pulse oximetry: simulation andJPhys photonics · 2026Article
- Towards real-time diffuse optical tomography with a handheld scanning probe.Biomedical optics express · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Significance: Frequency-domain diffuse optical tomography (FD-DOT) could enhance clinical breast tumor characterization. However, conventional diffuse optical tomography (DOT) image reconstruction algorithms require case-by-case expert tuning and are too computationally intensive to provide feedback during a scan. Deep learning (DL) algorithms front-load computational and tuning costs, enabling high-speed, high-fidelity FD-DOT. Aim: We aim to demonstrate a simultaneous reconstruction of three-dimensional absorption and reduced scattering coefficients using DL-FD-DOT, with a view toward real-time imaging with a handheld probe. Approach: A DL model was trained to solve the DOT inverse problem using a realistically simulated FD-DOT dataset emulating a handheld probe for human breast imaging and tested using both synthetic and experimental data. Results: Over a test set of 300 simulated tissue phantoms for absorption and scattering reconstructions, the DL-DOT model reduced the root mean square error by Conclusions: There is clinical potential for real-time functional imaging of human breast tissue using DL and FD-DOT.
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Registered trials
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