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

2025-11-05

AI Summary

High-speed 3D imaging of living hearts faces a fundamental trade-off between spatial and temporal resolution. Method: We propose a computational imaging framework integrating compressed sensing with light-sheet microscopy, employing a digital micromirror device (DMD) for hardware-based fluorescent signal encoding and compression, coupled with a plug-and-play alternating direction method of multipliers (PnP-ADMM) optimization algorithm. Crucially, we introduce inter-slice temporal regularization along the *z*-axis to enforce structural continuity of dynamic cardiac tissue and enable flexible incorporation of advanced denoising priors (e.g., BM3D, total variation). Contribution/Results: The method achieves robust reconstruction stability and high-fidelity subcellular detail recovery under high compression ratios, while minimizing phototoxicity. Experiments on beating zebrafish hearts demonstrate clear visualization of cellular structures at high compression, validating its efficacy and robustness for high-speed, low-light biological dynamic imaging.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Security and Privacy: Large-scale security measurements

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.

Problem

• Overcoming spatial-temporal resolution trade-off in cardiac imaging
• Reconstructing cellular structures from compressed fluorescence signals
• Enabling high-speed low-light imaging with reduced phototoxicity

Innovation

• Compressive sensing with light-sheet microscopy integration
• Plug-and-Play framework using ADMM optimization method
• Temporal regularization for structural continuity preservation