Fluorescence-illuminated Diffraction Tomography using Explicit Neural Fields.

Abstract

Multimodal imaging of fluorescence and phase provides distinct and complementary insights into biological samples. Current multimodal techniques for simultaneous phase and fluorescent imaging primarily operate in transmission mode and are limited to thin samples, restricting their applications to bulky tissues and in vivo animals. While multiphoton microscopy has enabled deeptissue fluorescence imaging, integrating it with phase imaging remains challenging due to the limited availability of methods capable of reconstructing the 3D refractive index (RI) of bulky, label-free tissues in reflection mode at subcellular resolution. To bridge the technical gap, we develop fluorescence-illuminated diffraction tomography (FDT) that reconstructs the 3D RI of label-free objects from diffracted fluorescence images acquired in reflection mode under two-photon excitation. The RI reconstruction leverages the transport of intensity equation (TIE) and is solved by a self-supervised neural network based on explicit neural fields. Compared to the state-of-the-art implicit neural fields, the explicit neural fields significantly improve computational speed, reconstruction accuracy, and interpretability. Using FDT, we successfully reconstruct the 3D RI of a 300 µmthick label-free bovine myotube sample over a 530 × 530 µm2 field-of-view at subcellular resolution within 20 min. FDT is the first technique to extract 3D RI from diffracted fluorescence images in reflection mode for thick tissues, overcoming key limitations of existing multimodal systems. This work lays the foundation for broadly accessible, reflection-mode multimodal fluorescence-phase imaging in complex biological systems in the future.