Fluorescence-illuminated diffraction tomography using explicit neural fields
Abstract
Multimodal fluorescence and phase imaging provides complementary information about biological samples. However, existing simultaneous fluorescence-phase techniques primarily operate in transmission mode and are limited to thin specimens, restricting their use in thick tissues and in vivo applications. Although multiphoton microscopy enables deep-tissue fluorescence imaging, combining it with three-dimensional refractive-index (RI) imaging remains challenging because few methods can reconstruct the RI of bulky, label-free tissues in reflection mode at subcellular resolution. Here, we introduce fluorescence-illuminated diffraction tomography (FDT), which reconstructs the 3D RI of label-free and fluorescence-labeled objects from diffracted fluorescence images acquired in reflection mode under two-photon excitation. FDT uses the transport of intensity equation and a self-supervised neural network based on explicit neural fields. Compared with state-of-the-art implicit neural fields, this approach improves computational speed, reconstruction accuracy, and interpretability. We demonstrate FDT in two experimental regimes: reconstructing the 3D RI of label-free tissues up to 300 μ m thick placed on a fluorescent substrate, and reconstructing the 3D RI of fluorescence-labeled, 3D-cultured cells without transmission-side access using fluorescence emitted by the cells themselves. FDT establishes a foundation for accessible reflection-mode multimodal fluorescence-phase imaging in complex biological systems.
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BibTeX
@article{he2026fluorescenceilluminated,
title = {{Fluorescence-illuminated diffraction tomography using explicit neural fields}},
author = {Renzhi He and Yucheng Li and Junjie Chen and Truc M. Dinh and Charlotte R. Stevens and Aidan E. Gilchrist and Yi Xue},
journal = {Nature Communications},
year = {2026},
month = oct,
doi = {10.1038/s41467-026-78326-x},
url = {https://www.nature.com/articles/s41467-026-78326-x},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Fluorescence-illuminated diffraction tomography using explicit neural fields
AU - Renzhi He
AU - Yucheng Li
AU - Junjie Chen
AU - Truc M. Dinh
AU - Charlotte R. Stevens
AU - Aidan E. Gilchrist
AU - Yi Xue
PY - 2026
DA - 2026/10/10
JO - Nature Communications
DO - 10.1038/s41467-026-78326-x
UR - https://www.nature.com/articles/s41467-026-78326-x
AB - Multimodal fluorescence and phase imaging provides complementary information about biological samples. However, existing simultaneous fluorescence-phase techniques primarily operate in transmission mode and are limited to thin specimens, restricting their use in thick tissues and in vivo applications. Although multiphoton microscopy enables deep-tissue fluorescence imaging, combining it with three-dimensional refractive-index (RI) imaging remains challenging because few methods can reconstruct the RI of bulky, label-free tissues in reflection mode at subcellular resolution. Here, we introduce fluorescence-illuminated diffraction tomography (FDT), which reconstructs the 3D RI of label-free and fluorescence-labeled objects from diffracted fluorescence images acquired in reflection mode under two-photon excitation. FDT uses the transport of intensity equation and a self-supervised neural network based on explicit neural fields. Compared with state-of-the-art implicit neural fields, this approach improves computational speed, reconstruction accuracy, and interpretability. We demonstrate FDT in two experimental regimes: reconstructing the 3D RI of label-free tissues up to 300 μ m thick placed on a fluorescent substrate, and reconstructing the 3D RI of fluorescence-labeled, 3D-cultured cells without transmission-side access using fluorescence emitted by the cells themselves. FDT establishes a foundation for accessible reflection-mode multimodal fluorescence-phase imaging in complex biological systems.
SN - 2041-1723
ER -