用AI从低剂量PET扫描重建高质量图像，显著提升清晰度与临床可用性。
Cycle-Constrained Adversarial Denoising Convolutional Network for PET Image Denoising: Multi-Dimensional Validation on Large Datasets with Reader Study and Real Low-Dose Data
- 引入循环约束的对抗性去噪网络，融合多损失函数优化图像质量。
- 在三种低剂量条件下，PSNR提升最高达56%，SSIM提升35%，NRMSE降低71%。
- 医生评测显示重建图像最接近全剂量图像，适合临床应用。
正电子发射断层成像（PET）是诊断肿瘤和神经疾病的重要工具，但存在患者辐射风险，尤其对敏感人群。降低注射辐射剂量虽可减小风险，却常导致图像质量下降。为从低剂量扫描中重建出全剂量水平的图像，本文提出循环约束的对抗性去噪卷积网络（Cycle-DCN）。该模型包含噪声预测器、两个判别器和一致性网络，通过监督损失、对抗损失、循环一致性损失、身份损失及邻域结构相似性指数（SSIM）损失联合优化。实验基于1,224名患者的脑部PET数据，使用Siemens Biograph Vision PET/CT扫描仪采集，每例扫描120秒。通过缩短扫描时间至30、12和5秒（分别对应全剂量的1/4、1/10、1/24），模拟低剂量条件。结果表明，Cycle-DCN在三个剂量水平下均显著提升平均峰值信噪比（PSNR）、结构相似性指数（SSIM）和归一化均方根误差（NRMSE），提升幅度最高达56%、35%和71%。同时，对比噪声比（CNR）和边缘保持指数（EPI）接近全剂量图像，有效保留了图像细节、病灶形状与对比度，解决了边缘模糊问题。读者研究结果显示，核医学医师对Cycle-DCN重建图像评分最高，凸显其强临床相关性。
原文摘要 · Abstract (English)
Positron emission tomography (PET) is a critical tool for diagnosing tumors and neurological disorders but poses radiation risks to patients, particularly to sensitive populations. While reducing injected radiation dose mitigates this risk, it often compromises image quality. To reconstruct full-dose-quality images from low-dose scans, we propose a Cycle-constrained Adversarial Denoising Convolutional Network (Cycle-DCN). This model integrates a noise predictor, two discriminators, and a consistency network, and is optimized using a combination of supervised loss, adversarial loss, cycle consistency loss, identity loss, and neighboring Structural Similarity Index (SSIM) loss. Experiments were conducted on a large dataset consisting of raw PET brain data from 1,224 patients, acquired using a Siemens Biograph Vision PET/CT scanner. Each patient underwent a 120-seconds brain scan. To simulate low-dose PET conditions, images were reconstructed from shortened scan durations of 30, 12, and 5 seconds, corresponding to 1/4, 1/10, and 1/24 of the full-dose acquisition, respectively, using a custom-developed GPU-based image reconstruction software. The results show that Cycle-DCN significantly improves average Peak Signal-to-Noise Ratio (PSNR), SSIM, and Normalized Root Mean Square Error (NRMSE) across three dose levels, with improvements of up to 56%, 35%, and 71%, respectively. Additionally, it achieves contrast-to-noise ratio (CNR) and Edge Preservation Index (EPI) values that closely align with full-dose images, effectively preserving image details, tumor shape, and contrast, while resolving issues with blurred edges. The results of reader studies indicated that the images restored by Cycle-DCN consistently received the highest ratings from nuclear medicine physicians, highlighting their strong clinical relevance.
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