用扩散模型填补缺失的PET数据，实现无限制探测器布局。
Diffusion-based Sinogram Interpolation for Limited Angle PET
- 用条件扩散模型预测不完整投影数据中的缺失信息。
- 可在严重欠采样情况下恢复高质量图像，支持开放结构探测器。
- 适合追求低成本、舒适扫描体验的临床PET系统设计。
精确的PET成像日益需要支持无约束探测器布局的方法，从穿行式设计到长轴向环形结构，其间隙和开放侧导致投影数据严重欠采样。与其限制硬件形成完整圆柱体，我们提出将缺失的响应线视为可学习先验。数据驱动方法，特别是生成模型，为恢复这些缺失信息提供了有前景的路径。本文探索使用条件扩散模型对稀疏采样的投影图进行插值，为实际临床环境中新型、低成本且患者友好的PET几何结构铺平道路。
原文摘要 · Abstract (English)
Accurate PET imaging increasingly requires methods that support unconstrained detector layouts from walk-through designs to long-axial rings where gaps and open sides lead to severely undersampled sinograms. Instead of constraining the hardware to form complete cylinders, we propose treating the missing lines-of-responses as a learnable prior. Data-driven approaches, particularly generative models, offer a promising pathway to recover this missing information. In this work, we explore the use of conditional diffusion models to interpolate sparsely sampled sinograms, paving the way for novel, cost-efficient, and patient-friendly PET geometries in real clinical settings.
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