Limited-angle tomography reconstruction via projector guided 3D diffusion

Limited-angle electron tomography (ET) 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-edge problem that causes severe reconstruction artefacts. Deep learning approaches have shown promising results in alleviating these artefacts, 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. In simulated ET datasets with limited angular coverage, TEMDiff outperforms the most advanced methods in reconstruction quality. We further demonstrate that a trained model TEMDiff generalizes well to real-world TEM tilts obtained under different conditions and can recover accurate structures from tilt ranges as narrow as 8 ∘ , with 2 ∘ increments, without any retraining or fine-tuning.