Efficient 3D Medical Reconstruction from Sparse Views via 2D Diffusion Models with Curvature Priors

Reconstructing medical images from partial measurements is a critical inverse problem in computed tomography (CT) and magnetic resonance imaging (MRI). While diffusion models excel in 2D medical imaging, their extension to 3D reconstruction remains challenging due to high computational costs. In this work, we achieve efficient 3D reconstruction from sparse views by reusing 2D diffusion models that are pre-trained unconditionally on 2D slices (CT for the CT tasks, MRI for the MRI tasks), without any 3D or task-specific training. Our method integrates curvature-regularized model-based iterative reconstruction with the denoising steps of diffusion models via the Alternating Direction Method of Multipliers (ADMM), where a z-axis curvature prior enforces inter-slice consistency and a variable-sharing scheme keeps the per-iteration cost close to that of purely 2D sampling. Experimental results show improved fine-detail and structural fidelity under sparse-view and limited-angle CT, with up to 15% higher PSNR and SSIM than baselines, and competitive MRI reconstruction across diverse datasets, including out-of-distribution settings. The proposed framework thus offers a computationally efficient route to high-fidelity 3D reconstruction, with promising clinical applications such as low-dose CT and accelerated MRI.