Cryo-ET Reconstruction
Missing-wedge restoration is, at heart, an inverse problem — self-supervised, ground-truth-free, solved with statistical machine learning.
Strengths & limits
What they do well: the reconstructions land closer to the real structure than IsoNet or DeepDeWedge, they put back detail the missing wedge had erased, and they need no ground-truth labels — CryoWGEN even tells you where it is least sure. The catch: CryoGEN gives you a single answer with no sense of how certain it is, and the part it fills into the wedge is inferred, not actually measured.
Articles in this base 5 articles
CryoGEN · one curve (point estimate)
CryoGEN-I: MAP estimation under an energy prior
Reconstruct each degraded tomogram into its single most probable clean volume — with an energy prior and the $P_Y$ proxy, and no ground truth at all
CryoGEN-II: distribution matching via optimal transport
Trading per-image optimality for global distribution alignment — using optimal transport to stabilize training and match the distribution of real structures
CryoWGEN · a family of curves (distribution)
CryoWGEN-I: Monte-Carlo sampling
Add an entropy term to the transport cost → a Boltzmann posterior; Monte-Carlo returns a family of reconstructions and makes the missing-wedge uncertainty readable
CryoWGEN-II: iterative Langevin sampling
Sample the Boltzmann posterior directly and iteratively with Langevin / SGLD — the same family, more faithful and tighter
The four methods — lineage & comparison
The true structure is two peaks (grey dashed). The missing wedge makes the gap between them ambiguous — and the four methods answer it differently. CryoGEN gives one definite answer, but it learns a GAN-style energy surface that carries bias: CryoGEN-I (MAP) deviates most and is overconfident; CryoGEN-II uses optimal transport to stabilize training and match the overall distribution, so it deviates less — but it is still a single deterministic answer. CryoWGEN switches to entropic regularization (EVIA), whose energy surface is smoother — its reconstructions sit closer to the truth, and instead of one answer it returns a family: CryoWGEN-I by Monte-Carlo (coarser, widely spread), CryoWGEN-II by Langevin / SGLD (the most faithful sampling, the tightest band). The width of that band is the missing-wedge uncertainty, made readable. Drag the slider — the more is missing, the more ambiguous the gap.