ArticleBiomedical optics express2023
Deep learning-based fusion of widefield diffuse optical tomography and micro-CT structural priors for accurate 3D reconstructions.
Article in Biomedical optics express, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Convolutional-transformer fusion of ultrasound and diffuse optical tomography for breast lesion classification.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2026Article
- Multi-spectral laser speckle contrast imaging for depth-resolved blood perfusion assessment.Journal of biomedical optics · 2025Article
- Deep-learning approach to stratified reconstructions of tissue absorption and scattering in time-domain spatial frequency domain imaging.Journal of biomedical optics · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
Widefield illumination and detection strategies leveraging structured light have enabled fast and robust probing of tissue properties over large surface areas and volumes. However, when applied to diffuse optical tomography (DOT) applications, they still require a time-consuming and expert-centric solving of an ill-posed inverse problem. Deep learning (DL) models have been recently proposed to facilitate this challenging step. Herein, we expand on a previously reported deep neural network (DNN) -based architecture (modified AUTOMAP - ModAM) for accurate and fast reconstructions of the absorption coefficient in 3D DOT based on a structured light illumination and detection scheme. Furthermore, we evaluate the improved performances when incorporating a micro-CT structural prior in the DNN-based workflow, named Z-AUTOMAP. This Z-AUTOMAP significantly improves the widefield imaging process's spatial resolution, especially in the transverse direction. The reported DL-based strategies are validated both
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Registered trials
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