Single-shot volumetric fluorescence imaging with neural fields

Abstract

Single-shot volumetric fluorescence (SVF) imaging offers a significant advantage over traditional imaging methods that require scanning across multiple axial planes as it can capture biological processes with high temporal resolution. The key challenges in SVF imaging include requiring sparsity constraints, eliminating depth ambiguity in the reconstruction, and maintaining high resolution across a large field of view. In this paper, we introduce the QuadraPol point spread function (PSF) combined with neural fields, a novel approach for SVF imaging. This method utilizes a custom polarizer at the back focal plane and a polarization camera to detect fluorescence, effectively encoding the 3D scene within a compact PSF without depth ambiguity. Additionally, we propose a reconstruction algorithm based on the neural fields technique that provides improved reconstruction quality compared to classical deconvolution methods. QuadraPol PSF, combined with neural fields, significantly reduces the acquisition time of a conventional fluorescence microscope by approximately 20 times and captures a 100 mm$^3$ cubic volume in one shot. We validate the effectiveness of both our hardware and algorithm through all-in-focus imaging of bacterial colonies on sand surfaces and visualization of plant root morphology. Our approach offers a powerful tool for advancing biological research and ecological studies.

Plant phenotyping relevance

植物根の形態を可視化する新規3D蛍光イメージング hardware と再構成アルゴリズムを開発・検証しており、植物フェノタイピング手法が中心である。

Code and data availability

The supplied blocks describe the QuadraPol PSF imaging system, neural-field reconstruction, and plant root phenotyping experiments, but contain no public dataset, image, code, or model deposit with an authors' URL. No qualifying paper-specific assets are present.

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