Autocorrelation analysis for cryo-EM with sparsity constraints: Improved sample complexity and projection-based algorithms.

ABSTRACT: The number of noisy images required for molecular reconstruction in single-particle cryoelectron microscopy (cryo-EM) is governed by the autocorrelations of the observed, randomly oriented, noisy projection images. In this work, we consider the effect of imposing sparsity priors on the molecule. We use techniques from signal processing, optimization, and applied algebraic geometry to obtain theoretical and computational contributions for this challenging nonlinear inverse problem with sparsity constraints. We prove that molecular structures modeled as sums of Gaussians are uniquely determined by the second-order autocorrelation of their projection images, implying that the sample complexity is proportional to the square of the variance of the noise. This theory improves upon the nonsparse

SUBMITTER: Bendory T 

PROVIDER: S-EPMC10161091 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature