用流模型融合物理先验，提升低剂量和金属伪影CT重建质量
Tomographic Foundation Model -- FORCE: Flow-Oriented Reconstruction Conditioning Engine
- 结合泊松流生成模型与医学成像物理约束
- 在无配对数据下仍实现优于现有方法的重建效果
- 适合临床低剂量扫描与金属伪影场景使用
计算机断层扫描（CT）是重要医学影像技术。临床中低剂量筛查、稀疏视角扫描及金属植入物常导致图像噪声和伪影严重，需改进重建方法。深度学习虽显著推进该领域，但因患者运动等因素难以获取配对训练数据，且即使使用近似配对数据也存在幻觉风险与模型不稳定性。本文将数据保真度与先进生成模型——广义泊松流生成模型（PFGM++）相结合，提出新型无监督重建框架：流导向重建条件引擎（FORCE）。实验表明，该方法在多种CT任务中均优于现有无监督重建方法。
原文摘要 · Abstract (English)
Computed tomography (CT) is a major medical imaging modality. Clinical CT scenarios, such as low-dose screening, sparse-view scanning, and metal implants, often lead to severe noise and artifacts in reconstructed images, requiring improved reconstruction techniques. The introduction of deep learning has significantly advanced CT image reconstruction. However, obtaining paired training data remains rather challenging due to patient motion and other constraints. Although deep learning methods can still perform well with approximately paired data, they inherently carry the risk of hallucination due to data inconsistencies and model instability. In this paper, we integrate the data fidelity with the state-of-the-art generative AI model, referred to as the Poisson flow generative model (PFGM) with a generalized version PFGM++, and propose a novel CT framework: Flow-Oriented Reconstruction Conditioning Engine (FORCE). In our experiments, the proposed method shows superior performance in various CT imaging tasks, outperforming existing unsupervised reconstruction approaches.
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