Diffusion-driven lensless fiber endomicroscopic quantitative phase imaging towards digital pathology

TL;DR

This paper introduces SpecDiffusion, a diffusion model-based method for high-fidelity, generalizable phase imaging in lensless fiber endomicroscopy, significantly improving complex microscopic image reconstruction and enabling advanced biological analysis.

Contribution

It proposes a novel speckle-conditioned diffusion model for direct phase reconstruction from fiber speckles, outperforming traditional neural networks in complex microscopic imaging tasks.

Findings

Achieved 7-fold reduction in mean absolute error for tissue phase images.

Demonstrated high accuracy in zero-shot cell segmentation tasks.

Validated generalization to unseen biological tissues and test charts.

Abstract

Lensless fiber endomicroscope is an emerging tool for in-vivo microscopic imaging, where quantitative phase imaging (QPI) can be utilized as a label-free method to enhance image contrast. However, existing single-shot phase reconstruction methods through lensless fiber endomicroscope typically perform well on simple images but struggle with complex microscopic structures. Here, we propose a speckle-conditioned diffusion model (SpecDiffusion), which reconstructs phase images directly from speckles captured at the detection side of a multi-core fiber (MCF). Unlike conventional neural networks, SpecDiffusion employs iterative phase denoising steps for speckle-driven phase reconstruction. The iteration scheme allows SpecDiffusion to break down the phase reconstruction process into multiple steps, gradually building up to the final phase image. This attribute alleviates the computation challenge at each step and enables the reconstruction of rich details in complex microscopic images. To validate its efficacy, we build an optical system to capture speckles from MCF and construct a dataset consisting of 100,000 paired images. SpecDiffusion provides high-fidelity phase reconstruction results and shows powerful generalization capacity for unseen objects, such as test charts and biological tissues, reducing the average mean absolute error of the reconstructed tissue images by 7 times. Furthermore, the reconstructed tissue images using SpecDiffusion shows higher accuracy in zero-shot cell segmentation tasks compared to the conventional method, demonstrating the potential for further cell morphology analysis through the learning-based lensless fiber endomicroscope. SpecDiffusion offers a precise and generalized method to phase reconstruction through scattering media, including MCFs, opening new perspective in lensless fiber endomicroscopic imaging.