用两个不同取向分布的数据，提升冷冻电镜三维重建精度
Two Datasets Are Better Than One: Method of Double Moments for 3-D Reconstruction in Cryo-EM
- 通过双阶矩融合框架，利用两种取向分布的投影图像
- 仅用二阶统计量即可精确恢复分子结构，误差极小
- 适合从事计算成像与生物结构解析的研究者
冷冻电镜（cryo-EM）是一种强大的成像技术，可通过随机取向粒子的噪声断层投影图像重建三维分子结构。本文提出一种新数据融合框架——双阶矩法（MoDM），利用在不同取向分布下获得的两组投影图像的二阶矩：一组均匀分布，另一组非均匀且未知。我们证明，这两个矩在一般情况下可唯一确定分子结构（仅允许全局旋转与反射）。同时，开发基于凸松弛的算法，仅使用二阶统计量即可实现高精度重建。结果表明，在不同实验条件下采集并建模多个数据集，能显著提升重建质量，凸显数据多样性在计算成像中的优势。
原文摘要 · Abstract (English)
Cryo-electron microscopy (cryo-EM) is a powerful imaging technique for reconstructing three-dimensional molecular structures from noisy tomographic projection images of randomly oriented particles. We introduce a new data fusion framework, termed the method of double moments (MoDM), which reconstructs molecular structures from two instances of the second-order moment of projection images obtained under distinct orientation distributions: one uniform, the other non-uniform and unknown. We prove that these moments generically uniquely determine the underlying structure, up to a global rotation and reflection, and we develop a convex-relaxation-based algorithm that achieves accurate recovery using only second-order statistics. Our results demonstrate the advantage of collecting and modeling multiple datasets under different experimental conditions, illustrating that leveraging dataset diversity can substantially enhance reconstruction quality in computational imaging tasks.
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