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.

翻译：心脏收缩是一个快速、协调的过程，在毫秒时间尺度上于三维组织中展开。传统光学成像因受限于空间分辨率与时间分辨率之间的固有权衡，往往难以捕捉搏动心脏中的动态细胞结构。为克服这些限制，我们提出了一种高性能计算成像框架，将压缩感知（CS）与光片显微镜（LSM）相结合，以实现高效、低光毒性的心脏成像。该系统通过数字微镜器件（DMD）采用随机二元掩模编码对荧光信号进行压缩采集。我们提出了一种基于乘子交替方向法（ADMM）求解的即插即用（PnP）框架，该框架灵活集成了包括Tikhonov、全变分（TV）和BM3D在内的先进去噪器。为保持动态成像中的结构连续性，我们进一步引入了时间正则化，以增强相邻z切片之间的平滑性。在斑马鱼心脏成像上的高压缩比实验结果表明，所提方法成功重建了细胞结构，具有优异的去噪性能和图像清晰度，验证了该算法在实际高速、低光生物成像场景中的有效性和鲁棒性。