用压缩感知提升冷冻电镜数据采集速度，保留原子级分辨率。
cryoSENSE: Compressive Sensing Enables High-throughput Microscopy with Sparse and Generative Priors on the Protein Cryo-EM Image Manifold
- 利用蛋白冷冻电镜图像的低维流形特性，结合稀疏与生成先验重建图像。
- 采集速度最高提升2.5倍，仍保持原始3D分辨率，支持可控压缩比。
- 适合需要高通量成像且对分辨率要求高的结构生物学研究者。
冷冻电子显微镜（cryo-EM）可实现生物分子的原子级可视化；然而，现代直接探测器产生的数据量远超存储与传输带宽，限制了实际通量。我们提出cryoSENSE，一种软硬件协同设计的压缩感知冷冻电镜传感与采集的计算实现框架。研究表明，蛋白质的冷冻电镜图像位于低维流形上，可通过预定义基下的稀疏先验和去噪扩散模型捕获的生成先验独立表示。cryoSENSE利用这些低维流形，从空间域和傅里叶域欠采样测量中实现高保真图像重建，同时保持下游结构分辨率。实验表明，cryoSENSE可将采集通量提高最多2.5倍，同时保留原始3D分辨率，提供测量掩码数量与下采样程度之间的可控权衡。稀疏先验在傅里叶域测量中表现更佳，适用于中等压缩；生成扩散先验则可在像素域测量中实现严重欠采样下的准确恢复。
原文摘要 · Abstract (English)
Cryo-electron microscopy (cryo-EM) enables the atomic-resolution visualization of biomolecules; however, modern direct detectors generate data volumes that far exceed the available storage and transfer bandwidth, thereby constraining practical throughput. We introduce cryoSENSE, the computational realization of a hardware-software co-designed framework for compressive cryo-EM sensing and acquisition. We show that cryo-EM images of proteins lie on low-dimensional manifolds that can be independently represented using sparse priors in predefined bases and generative priors captured by a denoising diffusion model. cryoSENSE leverages these low-dimensional manifolds to enable faithful image reconstruction from spatial and Fourier-domain undersampled measurements while preserving downstream structural resolution. In experiments, cryoSENSE increases acquisition throughput by up to 2.5$\times$ while retaining the original 3D resolution, offering controllable trade-offs between the number of masked measurements and the level of downsampling. Sparse priors favor faithful reconstruction from Fourier-domain measurements and moderate compression, whereas generative diffusion priors achieve accurate recovery from pixel-domain measurements and more severe undersampling. Project website: https://cryosense.github.io.
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