ArticleBiomedical optics express2020
Target depth-regularized reconstruction in diffuse optical tomography using ultrasound segmentation as prior information.
Article in Biomedical optics express, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 8 citations in OpenAlex.
- Latent diffusion-based image reconstruction for near-infrared spectral tomography.Biomedical optics express · 2026Article
- Automated pipeline for breast cancer diagnosis using US assisted diffuse optical tomography.Biomedical optics express · 2023Article
- Two-stage classification strategy for breast cancer diagnosis using ultrasound-guided diffuse optical tomography and deep learning.Journal of biomedical optics · 2023Article
- Resolution and penetration depth of reflection-mode time-domain near infrared optical tomography using a ToF SPAD camera.Biomedical optics express · 2022Article
- Difference imaging from single measurements in diffuse optical tomography: a deep learning approach.Journal of biomedical optics · 2022Article
- Machine learning model with physical constraints for diffuse optical tomography.Biomedical optics express · 2021Article
- Ultrasound segmentation-guided edge artifact reduction in diffuse optical tomography using connected component analysis.Biomedical optics express · 2021Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Ultrasound (US)-guided diffuse optical tomography (DOT) is a promising non-invasive functional imaging technique for diagnosing breast cancer and monitoring breast cancer treatment response. However, because larger lesions are highly absorbing, reconstructions of these lesions using reflection geometry may exhibit light shadowing, which leads to inaccurate quantification of their deeper portions. Here we propose a depth-regularized reconstruction algorithm combined with a semi-automated interactive neural network (CNN) for depth-dependent reconstruction of absorption distribution. CNN segments co-registered US to extract both spatial and depth priors, and the depth-regularized algorithm incorporates these parameters into the reconstruction. Through simulation and phantom data, the proposed algorithm is shown to significantly improve the depth distribution of reconstructed absorption maps of large targets. Evaluated with 26 patients with larger breast lesions, the algorithm shows 2.4 to 3 times improvement in the top-to-bottom reconstructed homogeneity of the absorption maps for these lesions.
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What OpenQuestion holds
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.