This paper introduces a novel framework for image quality transfer based on conditional flow matching (CFM). Unlike conventional generative models that rely on iterative sampling or adversarial objectives, CFM learns a continuous flow between a noise distribution and target data distributions through the direct regression of an optimal velocity field. We evaluate this approach in the context of low-field magnetic resonance imaging (LF-MRI), a rapidly emerging modality that offers affordable and portable scanning but suffers from inherently low signal-to-noise ratio and reduced diagnostic quality. Our framework is designed to reconstruct high-field-like MR images from their corresponding low-field inputs, thereby bridging the quality gap without requiring expensive infrastructure. Experiments demonstrate that CFM not only achieves state-of-the-art performance, but also generalizes robustly to both in-distribution and out-of-distribution data. Importantly, it does so while utilizing significantly fewer parameters than competing deep learning methods. These results underline the potential of CFM as a powerful and scalable tool for MRI reconstruction, particularly in resource-limited clinical environments.

翻译：本文提出了一种基于条件流匹配（CFM）的图像质量迁移新框架。与依赖迭代采样或对抗目标的传统生成模型不同，CFM通过直接回归最优速度场，学习噪声分布与目标数据分布之间的连续流。我们在低场磁共振成像（LF-MRI）背景下评估了该方法。LF-MRI作为一种快速兴起的新型成像模态，具有经济、便携的优势，但其固有的低信噪比导致诊断质量下降。本框架旨在从对应的低场输入中重建出类高场MR图像，从而在不依赖昂贵基础设施的前提下弥合质量鸿沟。实验表明，CFM不仅实现了最先进的性能，而且对分布内与分布外数据均展现出鲁棒的泛化能力。值得注意的是，该方法所需的参数量显著少于现有的深度学习方法。这些结果凸显了CFM作为MRI重建的强大可扩展工具的潜力，尤其在资源有限的临床环境中具有重要应用价值。