Self-supervised missing-wedge correction enables accurate cellular morphology and volume reconstruction in soft X-ray tomography
- Journal
- Cell reports methods (Q1)
- Published
- 28 September 2026
- Study design
- Unclassified
- Evidence level
- Level 5, Expert Opinion (CEBM 5)
- Authors
- Shao Sen Chueh, Mary Lopez-Perez, Charlotte de Ceuninck van Capelle, Takashi Ishikawa, Alessandro Zannotti, Victoria Castro, et al.
- PMID
- 42805181
- DOI
- 10.1016/j.crmeth.2026.101610
Why clinicians should know about it
- Picked for Histology (paper of the day, 29 September 2026): Self‑supervised missing‑wedge correction improves cellular morphology in soft X‑ray tomography
Abstract
Soft X-ray tomography (SXT) bridges the resolution gap between fluorescence and transmission electron microscopy for 3D cellular imaging. However, the missing-wedge artifact from incomplete tilt-series acquisition causes systematic structural elongation, thereby overestimating organelle volumes and compromising quantitative analysis. To address this, we introduce a self-supervised correction model that learns sine-wave trajectories from SXT sinograms to recover unrecorded missing-angle regions, effectively reducing tomographic distortion. We demonstrate significant quantitative improvement, restoring the spherical morphology of 500-nm beads and lipid droplets. When applied to Plasmodium falciparum hemozoin crystals, a key biomarker in antimalarial drug efficacy studies, our model successfully mitigated volume overestimation, achieving up to a 35% reduction in distorted volume. This precision is important for correctly interpreting the mode of action of antimalarial drugs targeting hemozoin crystals.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.