Limited-Angle Tomography Reconstruction via Projector Guided 3D Diffusion
This addresses reconstruction artifacts in electron microscopy for researchers, but it is incremental as it builds on existing deep learning and diffusion methods.
The paper tackles the missing-wedge problem in limited-angle electron tomography by proposing TEMDiff, a 3D diffusion-based iterative reconstruction framework, which outperforms state-of-the-art methods on simulated datasets and generalizes to real-world TEM tilts, recovering accurate structures from tilt ranges as narrow as 8 degrees.
Limited-angle electron tomography aims to reconstruct 3D shapes from 2D projections of Transmission Electron Microscopy (TEM) within a restricted range and number of tilting angles, but it suffers from the missing-wedge problem that causes severe reconstruction artifacts. Deep learning approaches have shown promising results in alleviating these artifacts, yet they typically require large high-quality training datasets with known 3D ground truth which are difficult to obtain in electron microscopy. To address these challenges, we propose TEMDiff, a novel 3D diffusion-based iterative reconstruction framework. Our method is trained on readily available volumetric FIB-SEM data using a simulator that maps them to TEM tilt series, enabling the model to learn realistic structural priors without requiring clean TEM ground truth. By operating directly on 3D volumes, TEMDiff implicitly enforces consistency across slices without the need for additional regularization. On simulated electron tomography datasets with limited angular coverage, TEMDiff outperforms state-of-the-art methods in reconstruction quality. We further demonstrate that a trained TEMDiff model generalizes well to real-world TEM tilts obtained under different conditions and can recover accurate structures from tilt ranges as narrow as 8 degrees, with 2-degree increments, without any retraining or fine-tuning.