用于修复不完整环PET图像的两阶段深度学习框架
Two-stage deep learning framework for the restoration of incomplete-ring PET images
AI总结：
提出一种两阶段深度学习框架，无需飞行时间信息，通过投影域注意力U-Net预测缺失正弦图部分和级联U-Net与热启动扩散模型进行图像细化，从约50%缺失符合事件的不完整环数据中恢复高质量PET图像。
摘要：
正电子发射断层扫描（PET）是一种重要的分子成像工具，广泛应用于医学。传统的PET系统依赖完整的探测器环来实现全角度覆盖和可靠的数据收集。然而，由于硬件故障、成本限制或特定临床需求，出现了不完整环PET扫描仪。标准重建算法由于数据完整性的降低和几何不一致性，在这些系统中往往性能下降。我们提出了一种两阶段深度学习框架，无需任何飞行时间（TOF）信息，即可从约50%缺失符合事件的数据中恢复高质量图像——这是之前基于CNN方法处理损失水平的两倍。该流程分两个阶段运行：投影域注意力U-Net首先通过利用相邻切片的空间上下文预测正弦图的缺失部分，然后使用OSEM算法重建完整数据，并将其传递给级联U-Net和热启动扩散模型进行图像细化。该模块从U-Net粗预测而非纯高斯噪声开始反向扩散过程。使用来自真实扫描的613个模拟脑体积（196个健康脑样本、217个阿尔茨海默病样本和200个轻度认知障碍样本），结果表明我们的模型成功保留了大部分解剖结构和示踪剂分布特征，PSNR为38.18至38.59 dB，SSIM为0.9904至0.9925。我们的两阶段深度学习框架有效地从超过50%的不完整环数据中恢复高质量PET图像，实现了接近完整的解剖保真度和鲁棒性能，无需TOF信息。
英文摘要：
Positron Emission Tomography (PET) is an important molecular imaging tool widely used in medicine. Traditional PET systems rely on complete detector rings for full angular coverage and reliable data collection. However, incomplete-ring PET scanners have emerged due to hardware failures, cost constraints, or specific clinical needs. Standard reconstruction algorithms often suffer from performance degradation with these systems because of reduced data completeness and geometric inconsistencies. We present a two-stage deep-learning framework that, without incorporating any time-of-flight (TOF) information, restores high-quality images from data with about 50% missing coincidences - double the loss levels previously addressed by CNN-based methods. The pipeline operates in two stages: a projection-domain Attention U-Net first predicts the missing sections of the sinogram by leveraging spatial context from neighbouring slices, after which the completed data are reconstructed with OSEM algorithm and passed to a cascaded U-Net & warm-start diffusion model for image refinement. This module starts the reverse diffusion process from the U-Net coarse prediction rather than pure Gaussian noise. Using 613 simulated brain volumes from real scans (196 healthy brain samples, 217 Alzheimer's disease samples, and 200 Mild Cognitive Impairment samples), the result shows that our model successfully preserves most anatomical structures and tracer distribution features with PSNR of 38.18 to 38.59 dB and SSIM of 0.9904 to 0.9925. Our two-stage deep-learning framework effectively restores high-quality PET images from over 50% incomplete-ring data, achieving near-complete anatomical fidelity and robust performance without requiring TOF information.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
