A latent back-projection network for novel projection synthesis for improved Cryo-ET

Robert Kiewisz, Gabriel Meyer-Lee, Tristan Bepler

Track: Machine learning: computational method and/or computational results

Nature Biotechnology: No

Keywords: Cryo-EM, projection synthesis, image restoration

TL;DR: This paper presents a novel proof-of-concept model that generates Cryo-ET projections at new angles, reducing the missing wedge by learning directly from tilt-series data in the Fourier domain, outperforming existing tools like IsoNet.

Abstract: Cryo-electron tomography (Cryo-ET) is hindered by the missing wedge, a gap in Fourier-space information caused by limited tilt-series angular coverage, leading to anisotropic resolution loss and artifacts. Current methods, such as IsoNet, attempt to "inpaint" missing frequencies in reconstructed tomograms but are constrained by their reliance on pre-degraded data, often producing non-physical features. We present our proof-of-concept model, a generative latent back-projection autoencoder that bypasses traditional tomogram reconstruction and directly synthesizes novel projections from tilt-series data in the frequency domain. Our latent back-projection network encoder-decoder architecture maps raw projections to a 3D Fourier volume, leveraging the Fourier slice theorem to generate high-fidelity projections beyond the experimental tilt range. Evaluated on \textit{E. coli} mini-cells, our model achieves a lower MSE and higher correlation with ground-truth data. Crucially, our model robustly recovers withheld tilts (0° or ±15°) without retraining, outperforming IsoNet in MSE and correlation metrics. By mitigating the missing wedge through generating tilts at new angles, our proof-of-concept can potentially advances high-resolution in situ structural biology for radiation-sensitive specimens.

Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.

Presenter: ~Gabriel_Meyer-Lee1

Format: Yes, the presenting author will attend in person if this work is accepted to the workshop.

Funding: No, the presenting author of this submission does *not* fall under ICLR’s funding aims, or has sufficient alternate funding.

Submission Number: 117