用神经网络提升超分辨PET的探测灵敏度，不牺牲分辨率
Machine Learning-Based Inter-Crystal Scatter Recovery for Ultra-High Resolution PET Imaging
- 用前馈神经网络预测伽马射线首次康普顿散射位置
- 灵敏度提升70%~106%，空间分辨率达1.6毫米
- 适合需要高精度、低剂量成像的临床与科研场景
晶体间散射（ICS）事件是超分辨率正电子发射断层扫描（UHR-PET）中的主要挑战，尤其在探测晶体更小、读出通道更细分的情况下。现有方法要么拒绝这些事件导致灵敏度下降，要么采用次优定位算法，降低图像分辨率。本文提出一种前馈神经网络，用于优化ICS事件恢复，通过推断首次康普顿散射对应的直线响应（line-of-response）。该方法在基于全像素化LabPET-II的临床与脑部UHR-PET扫描仪上，通过蒙特卡洛模拟和实验数据验证。结果表明，相比传统方法，灵敏度提高70%至106%，同时保持亚毫米级空间分辨能力（最低达1.6 mm）。该方法有效弥补了小型像素化探测器检测效率低的问题，可在大幅缩短扫描时间的同时降低辐射剂量，且基本保持图像质量。
原文摘要 · Abstract (English)
Inter-crystal scatter (ICS) events pose a significant challenge in ultrahigh- resolution positron emission tomography (UHR-PET), especially as detector crystals become smaller and their readouts increasingly segmented. Current approaches either reject these events, reducing sensitivity, or accept them with suboptimal positioning algorithms, degrading image resolution. We present a feed forward neural network to optimize ICS event recovery by inferring the line-of-response belonging to the first Compton interaction. Our approach was validated using both Monte Carlo simulations and experimental data from the fully pixelated LabPET-IIbased preclinical and brain UHR-PET scanners.Results demonstrate a 70% to 106% increase in sensitivity while preserving sub-millimeter spatial resolvability (down to 1.6 mm) compared to conventional methods. This ICS recovery approach is an effective solution that compensates for the lower detection efficiency of small, pixelated detectors in UHR-PET, enabling reduced scan times and lower radiation doses while largely preserving image quality.
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