Computational TIRF enables optical sectioning beyond the evanescent field for widefield fluorescence microscopy

TL;DR

Computational TIRF (cTIRF) uses deep learning to produce TIRF-like images from standard widefield fluorescence data, enabling optical sectioning beyond the shallow depth limit of traditional TIRF microscopy.

Contribution

This work introduces a physics-informed deep learning method that achieves optical sectioning from conventional widefield images without optical modifications, surpassing traditional TIRF limitations.

Findings

cTIRF effectively suppresses background and enhances axial resolution.

It recovers near-surface structures comparable to experimental TIRF.

Enables volumetric reconstruction in densely labeled samples.

Abstract

The resolving ability of widefield fluorescence microscopy is fundamentally limited by out-of-focus background owing to its low axial resolution, particularly for densely labeled biological samples. Although total internal reflection fluorescence (TIRF) microscopy provides strong near-surface sectioning, they are intrinsically restricted to shallow imaging depths. Here we present computational TIRF (cTIRF), a deep learning-based imaging modality that generates TIRF-like sectioned images directly from conventional widefield epifluorescence measurements without any optical modification. By integrating a physics-informed forward model into network training, cTIRF achieves effective background suppression and axial resolution enhancement while maintaining consistency with the measured widefield data. We demonstrate that cTIRF recovers near-surface structures with performance comparable to experimental TIRF, and further enables both single-frame and volumetric sectioned reconstruction in densely labeled samples where conventional TIRF fails. This work establishes cTIRF as a practical and deployable alternative to hardware-based optical sectioning in fluorescence microscopy, enabled by rapid adaptation to new imaging systems with minimal calibration data.