Advanced Brain Tissue Imaging with Data-Consistent Diffusion Priors in Laminographic X-Ray Nanoimaging

2026-09-09

AI Summary

为解决X射线层析成像中因数据缺失导致的脑组织成像失真问题，提出LUCID框架，结合多视角扩散先验与投影域数据一致性方法恢复未测量信息。

Abstract

Nanoscale imaging of mammalian brains is critical for connectomics. X-ray laminography enables high-throughput imaging of extended, plate-like biological specimens. However, the tilted acquisition geometry leads to incomplete Fourier-space coverage, giving rise to a missing-cone of information. Conventional reconstruction methods cannot recover unmeasured information within the cone, resulting in artifacts that distort fine brain structures. While resolving these requires modeling 3D structure, direct 3D deep learning approaches are limited by data scarcity and computational cost. Here we introduce LUCID (Laminography with Unified Consistent Diffusion), a framework that combines multi-view diffusion priors with projection-domain data consistency. LUCID integrates complementary 3D structural information while enforcing strict alignment with the laminography forward model. On simulated datasets, LUCID substantially improves spatial fidelity and restores missing Fourier components, outperforming baseline methods. Applied to experimental laminography data, LUCID generalizes robustly despite being trained exclusively on fully sampled tomographic volumes, and effectively recovers unmeasured Fourier information.

Problem

• missing-cone
• laminography
• Fourier-space
• brain imaging
• nanoscale

Innovation

• Data-Consistent Diffusion Priors
• Laminographic X-Ray Nanoimaging
• Unified Consistent Diffusion
• Multi-view Diffusion Priors
• Projection-domain Data Consistency