[2603.00205] Efficient Flow Matching for Sparse-View CT Reconstruction

Electrical Engineering and Systems Science > Image and Video Processing arXiv:2603.00205 (eess) [Submitted on 27 Feb 2026] Title:Efficient Flow Matching for Sparse-View CT Reconstruction Authors:Jiayang Shi, Lincen Yang, Zhong Li, Tristan Van Leeuwen, Daniel M. Pelt, K. Joost Batenburg View a PDF of the paper titled Efficient Flow Matching for Sparse-View CT Reconstruction, by Jiayang Shi and 5 other authors View PDF HTML (experimental) Abstract:Generative models, particularly Diffusion Models (DM), have shown strong potential for Computed Tomography (CT) reconstruction serving as expressive priors for solving ill-posed inverse problems. However, diffusion-based reconstruction relies on Stochastic Differential Equations (SDEs) for forward diffusion and reverse denoising, where such stochasticity can interfere with repeated data consistency corrections in CT reconstruction. Since CT reconstruction is often time-critical in clinical and interventional scenarios, improving reconstruction efficiency is essential. In contrast, Flow Matching (FM) models sampling as a deterministic Ordinary Differential Equation (ODE), yielding smooth trajectories without stochastic noise injection. This deterministic formulation is naturally compatible with repeated data consistency operations. Furthermore, we observe that FM-predicted velocity fields exhibit strong correlations across adjacent steps. Motivated by this, we propose an FM-based CT reconstruction framework (FMCT) and an efficient v...