mlsanityData to 7 Oct 2026

Boosted ab initio Cryo-EM 3D Reconstruction with ACE-EM

arXiv 2302.06091 ↗First posted 13 Feb 2023cs.CV cs.AI cs.LGcited by 1

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Abstract (arXiv)

The central problem in cryo-electron microscopy (cryo-EM) is to recover the 3D structure from noisy 2D projection images which requires estimating the missing projection angles (poses). Recent methods attempted to solve the 3D reconstruction problem with the autoencoder architecture, which suffers from the latent vector space sampling problem and frequently produces suboptimal pose inferences and inferior 3D reconstructions. Here we present an improved autoencoder architecture called ACE (Asymmetric Complementary autoEncoder), based on which we designed the ACE-EM method for cryo-EM 3D reconstructions. Compared to previous methods, ACE-EM reached higher pose space coverage within the same training time and boosted the reconstruction performance regardless of the choice of decoders. With this method, the Nyquist resolution (highest possible resolution) was reached for 3D reconstructions of both simulated and experimental cryo-EM datasets. Furthermore, ACE-EM is the only amortized inference method that reached the Nyquist resolution.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 37 references that are in the map. First posted on arXiv 13 Feb 2023.

Builds on

17 of 37 references.

References · 17

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5 more
  1. 2019Reconstructing continuous distributions of 3D protein structure from cryo-EM imagescited by 56
  2. 2022CryoAI: Amortized Inference of Poses for Ab Initio Reconstruction of 3D Molecular Volumes from Real Cryo-EM Imagescited by 24
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Cited by year

Citing papers per year: 2023: 1.

1 paper.

  1. 2023CryoGEM: Physics-Informed Generative Cryo-Electron Microscopycited by 4