用测试时自适应让PET图像重建模型快速适配真实临床数据。
PET-Adapter: Test-Time Domain Adaptation for Full and Limited-Angle PET Image Reconstruction
- 通过分层低秩解剖条件与物理引导初始化，实现无需真值的测试时域自适应。
- 将扩散步骤从50次减少到2次，重建质量不下降，计算效率显著提升。
- 适用于不同解剖结构、示踪剂和扫描仪配置的真实临床数据，泛化能力强。
正电子发射断层成像（PET）图像重建受泊松噪声及物理退化因素影响，尤其在有限角度采集时更为严重。尽管深度学习方法表现良好，但其在未见临床数据分布上的泛化能力仍受限，需大量重训练。本文提出PET-Adapter，一种针对仅在体模数据上预训练的生成式PET重建模型的测试时域自适应框架。该方法无需配对真值即可适应临床数据，涵盖不同解剖结构、示踪剂和扫描仪配置。PET-Adapter引入分层低秩解剖条件机制，并采用基于有序子集期望最大化（OS-MAP）的热启动策略，从物理信息重建初始化生成过程，使扩散步骤由50次降至2次而质量不变。在多个临床数据集上的实验表明，该方法在全角和有限角设置下均实现更优的3D重建性能，验证了其临床可行性与计算高效性。
原文摘要 · Abstract (English)
Positron Emission Tomography (PET) image reconstruction is inherently challenged by Poisson noise and physical degradation factors, which are further exacerbated in limited-angle acquisitions. While deep learning methods demonstrate promising performance, their generalization to unseen clinical data distributions remains limited without extensive retraining. We propose PET-Adapter, a test-time domain adaptation framework for generative PET reconstruction models pretrained solely on phantom data. Our method enables adaptation to clinical datasets with varying anatomies, tracers, and scanner configurations without requiring paired ground truth. PET-Adapter introduces layer-wise low-rank anatomical conditioning during adaptation and Ordered Subset Expectation Maximization-based warm-starting that initializes the generation from physics-informed reconstructions, reducing diffusion steps from 50 to 2 without compromising quality. Experiments across multiple clinical datasets demonstrate superior 3D reconstruction performance in both full-angle and limited-angle settings, highlighting the clinical feasibility and computational efficiency of the proposed approach.
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