发表机构
University of Bologna（博洛尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出物理引导的流匹配方法，训练修正流匹配模型，经两阶段策略优化后，其在CT图像重建中性能优于扩散模型，且计算效率更高，相关模型与代码已公开。
AI 中文摘要
深度生成模型近年来已成为解决CT中不适定逆问题的强大先验，基于扩散的方法实现了最先进的重建性能。然而，扩散模型通常依赖随机采样过程、长推理轨迹以及精心调整的噪声调度，这会限制计算效率和数值稳定性，尤其是在高空间分辨率下。在这项工作中，我们研究流匹配作为CT重建的替代生成先验。我们在Mayo Clinic低剂量CT数据集的256×256胸部图像上训练了一个高分辨率的修正流匹配（Rectified Flow Matching）模型。为缓解过拟合和有限的解剖变异性，我们采用两阶段训练策略：初始阶段采用强的、基于解剖学信息的数据增强，随后的微调阶段使用减少或不使用增强来优化结构保真度。所得模型能够生成高质量且解剖学一致的类CT图像，可作为强大的学习先验。然后，我们评估了多种专为流匹配模型设计的重建方法，包括Plug-and-Play Flow、FlowDPS、Flower和Flow-Priors（ICTM），并将它们与DDRM、DPS和DiffPIR等最先进的基于扩散的重建算法进行比较。在多种CT逆问题设置下的实验结果表明，基于流匹配的方法在PSNR、SSIM和感知质量方面始终优于基于扩散的方法，同时需要更少的采样步数。最后，我们公开发布了训练好的流匹配模型和配套代码，以促进可复现性和未来研究。总体而言，这项工作证明流匹配为高分辨率CT图像重建提供了一种稳定、高效且有效的替代扩散模型的方案。
英文摘要
Deep generative models have recently emerged as powerful priors for solving ill-posed inverse problems in CT, with diffusion-based approaches achieving state-of-the-art reconstruction performance. However, diffusion models typically rely on stochastic sampling procedures, long inference trajectories, and carefully tuned noise schedules, which can limit computational efficiency and numerical stability, especially at high spatial resolutions. In this work, we investigate Flow Matching as an alternative generative prior for CT reconstruction. We train a high-resolution Rectified Flow Matching model on 256x256 chest images from the Mayo Clinic Low-Dose CT dataset. To mitigate overfitting and limited anatomical variability, we employ a two-stage training strategy consisting of an initial phase with strong, anatomically informed data augmentation, followed by a fine-tuning phase with reduced or no augmentation to refine structural fidelity. The resulting model is capable of generating high-quality and anatomically coherent CT-like images, serving as a strong learned prior. We then evaluate multiple reconstruction methods specifically designed for Flow Matching models, including Plug-and-Play Flow, FlowDPS, Flower, and Flow-Priors (ICTM), and compare them against state-of-the-art diffusion-based reconstruction algorithms such as DDRM, DPS, and DiffPIR. Experimental results across several CT inverse problem settings show that Flow Matching-based approaches consistently outperform diffusion-based methods in terms of PSNR, SSIM, and perceptual quality, while requiring fewer sampling steps. Finally, we publicly release the trained Flow Matching model and accompanying code to facilitate reproducibility and future research. Overall, this work demonstrates that Flow Matching provides a stable, efficient, and effective alternative to diffusion models for high-resolution CT image reconstruction.
Comments16 pages, 6 figures, 2 tables
