用AI降噪技术让低计数PET图像达到高精度，减少辐射和成本。
Maintaining SUV Accuracy in Low-Count PET with PETfectior: A Deep Learning Denoising Solution
- 通过深度学习对低计数PET数据去噪，提升信噪比。
- 99.9%检出率，定量偏差低于12.5%，仅1个假阳性。
- 适合追求低辐射、低成本的临床PET检查场景。
诊断性PET图像质量依赖于注射剂量和采集时间，但降低这些参数可减少患者辐射暴露和放射药剂成本。PETfectior是一种基于人工智能的软件，可处理PET扫描数据，提高信噪比，从低计数图像中生成高质量影像。本研究对半计数统计（仅为最新EANM 18F-FDG PET定量标准所需的一半）采集的图像进行初步临床验证，评估病灶检出率、定量性能及图像质量。共纳入258例18F-FDG PET/CT受检者。标准扫描（100%扫描）按EARL标准2重建；半计数统计图像由列表模式数据生成，并经PETfectior处理（50%+PETfectior）。所有肿瘤病灶在两种图像上手动或自动分割，评估检出能力。测量病灶SUVmax，分析50%+PETfectior与100%图像间的定量一致性。两名经验丰富的医生主观评估图像质量。结果显示，在198项研究中共检出1649个病灶。50%+PETfectior图像具有高检出敏感性（99.9%），仅1个假阳性。100%与50%+PETfectior图像的SUVmax一致性在12.5%以内（95%一致性界限），偏差为-1.01%。50%+PETfectior图像的主观质量等同或优于标准扫描。结论：PETfectior可在半计数统计条件下安全用于临床，具备高灵敏度与特异性、低定量偏差及高主观图像质量。
原文摘要 · Abstract (English)
Background: Diagnostic PET image quality depends on the administered activity and acquisition time. However, minimizing these variables is desirable to reduce patient radiation exposure and radiopharmaceutical costs. PETfectior is an artificial intelligence-based software that processes PET scans and increases signal-to-noise ratio, obtaining high-quality images from low-count-rate images. We perform an initial clinical validation of PETfectior on images acquired with half of the counting statistics required to meet the most recent EANM quantitative standards for 18F-FDG PET, evaluating lesions detectability, quantitative performance and image quality. Materials and methods: 258 patients referred for 18F-FDG PET/CT were prospectively included. The standard-of-care scans (100% scans) were acquired and reconstructed according to EARL standards 2. Half-counting-statistics versions were generated from list-mode data and processed with PETfecftior (50%+PETfectior scans). All oncologic lesions were segmented on both PET/CT versions, manually or automatically, and lesions detectability was evaluated. The SUVmax of the lesions was measured and the quantitative concordance of 50%+PETfectior and 100% images was evaluated. Subjective image quality was visually assessed by two experienced physicians. Results: 1649 lesions were detected in a total of 198 studies. The 50%+PETfectior images showed high sensitivity for lesion detection (99.9%) and only 1 false positive was detected. The SUVmax measured in 100% and 50%+PETfectior images agreed within 12.5% (95% limits of agreement), with a bias of -1.01%. Image quality of the 50%+PETfectior images was rated equal to or better than the standard-of-care images. Conclusion: PETfectior can safely be used in clinical practice at half counting statistics, with high sensitivity and specificity, low quantitative bias and high subjective image quality.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。