ArticleBiomedical optics express2024
Diffuse optical tomography of the brain: effects of inaccurate baseline optical parameters and refinements using learned post-processing.
Article in Biomedical optics express, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 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
- Noninvasive prediction of photon fluence rate in targeted brain regions for personalized transcranial photobiomodulation dosage: model development andBiomedical optics express · 2025Article
- DOT-AE-GAN: a hybrid autoencoder-GAN model for enhanced ultrasound-guided diffuse optical tomography reconstruction.Journal of biomedical optics · 2025Article
- From morphology to single-cell molecules: high-resolution 3D histology in biomedicine.Molecular cancer · 2025Review
- Simultaneous Mental Fatigue and Mental Workload Assessment With Wearable High-Density Diffuse Optical Tomography.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2025Article
- Diffuse Optical Spectroscopy: Technology and Applications: introduction to the feature issue.Biomedical optics express · 2024Article
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Authors and funding
6 authors.
Funding
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Abstract
Diffuse optical tomography (DOT) uses near-infrared light to image spatially varying optical parameters in biological tissues. In functional brain imaging, DOT uses a perturbation model to estimate the changes in optical parameters, corresponding to changes in measured data due to brain activity. The perturbation model typically uses approximate baseline optical parameters of the different brain compartments, since the actual baseline optical parameters are unknown. We simulated the effects of these approximate baseline optical parameters using parameter variations earlier reported in literature, and brain atlases from four adult subjects. We report the errors in estimated activation contrast, localization, and area when incorrect baseline values were used. Further, we developed a post-processing technique based on deep learning methods that can reduce the effects due to inaccurate baseline optical parameters. The method improved imaging of brain activation changes in the presence of such errors.
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Registered trials
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