Subspace method of moments for ab initio 3-D single-particle Cryo-EM reconstruction
<|reference_start|>Subspace method of moments for ab initio 3-D single-particle Cryo-EM reconstruction: Cryo-electron microscopy (Cryo-EM) is a widely-used technique for recovering the 3-D structure of biological molecules from a large number of experimentally generated noisy 2-D tomographic projection images of the 3-D structure, taken from unknown viewing angles. Through computationally intensive algorithms, these observed images are processed to reconstruct the 3-D structures. Many popular computational methods rely on estimating the unknown angles as part of the reconstruction process, which becomes particularly challenging at low signal-to-noise ratio. The method of moments (MoM) offers an alternative approach that circumvents the estimation of viewing angles of individual projection images by instead estimating the underlying distribution of the viewing angles, and is robust to noise given sufficiently many images. However, the method of moments typically entails computing high-order moments of the projection images, incurring significant storage and computational costs.   To mitigate this, we propose a new approach called the subspace method of moments (subspace MoM), which compresses the first three moments using data-driven low-rank tensor techniques as well as expansion into a suitable function basis. The compressed moments can be efficiently computed from the set of projection images using numerical quadrature and can be employed to jointly recover the 3-D structure and the distribution of viewing angles. We illustrate the practical applicability of the subspace MoM in numerical experiments using up to the third-order moment, which significantly improves the resolution of MoM reconstructions compared to previous approaches.<|reference_end|>
@article{hoskins2024subspace,
    title={Subspace method of moments for ab initio 3-D single-particle Cryo-EM
  reconstruction},
    author={Jeremy Hoskins, Yuehaw Khoo, Oscar Mickelin, Amit Singer, Yuguan Wang},
    journal={arXiv preprint arXiv:2410.06889},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.06889},
    primaryClass={math.NA cs.NA}
}