A Plug-and-Play Framework for Volumetric Light-Sheet Image Reconstruction

TL;DR

This work tackles the challenge of high-fidelity 3D imaging of rapid cardiac dynamics under low phototoxicity. It integrates Compressive Sensing with Light-Sheet Microscopy by encoding depth planes with random binary masks on a DMD and solving the ensuing ill-posed inverse problem with a Plug-and-Play ADMM framework that accommodates diverse denoisers. A novel temporal regularization is introduced to enforce smoothness across adjacent z-slices, and the approach includes efficient computations via Woodbury inverses and Gauss-Seidel updates. Through synthetic zebrafish heart data, the authors demonstrate that temporal, non-local priors like BM3D achieve the best reconstruction quality under both noise-free and noisy conditions, with temporal modeling consistently outperforming slice-only reconstructions; the framework is flexible enough to incorporate learned priors and could enable 4D volumetric reconstructions of the beating heart with high spatiotemporal fidelity.

Abstract

Cardiac contraction is a rapid, coordinated process that unfolds across three-dimensional tissue on millisecond timescales. Traditional optical imaging is often inadequate for capturing dynamic cellular structure in the beating heart because of a fundamental trade-off between spatial and temporal resolution. To overcome these limitations, we propose a high-performance computational imaging framework that integrates Compressive Sensing (CS) with Light-Sheet Microscopy (LSM) for efficient, low-phototoxic cardiac imaging. The system performs compressed acquisition of fluorescence signals via random binary mask coding using a Digital Micromirror Device (DMD). We propose a Plug-and-Play (PnP) framework, solved using the alternating direction method of multipliers (ADMM), which flexibly incorporates advanced denoisers, including Tikhonov, Total Variation (TV), and BM3D. To preserve structural continuity in dynamic imaging, we further introduce temporal regularization enforcing smoothness between adjacent z-slices. Experimental results on zebrafish heart imaging under high compression ratios demonstrate that the proposed method successfully reconstructs cellular structures with excellent denoising performance and image clarity, validating the effectiveness and robustness of our algorithm in real-world high-speed, low-light biological imaging scenarios.

Figures (4)

• Figure 1: Design of CS-LSM imaging formation.
• Figure 2: Image reconstruction from noise-free data, the 17th slice is plotted.
• Figure 3: Image reconstruction from noisy data, the 17th slice is plotted.
• Figure 4: PSNR values for noise-free reconstructions at different compression ratios.