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<p>Quantitative PET underpins diagnosis and treatment monitoring in neurodegenerative disease, yet systematic biases between PET-MRI and PET-CT preclude threshold transfer and cross-site comparability. We developed and validated the first unified, anatomically guided deep-learning framework to harmonize PET-MRI quantification to PET-CT standards across multiple tracers and scanner manufacturers. The model learns CT-anchored attenuation representations using a vision transformer autoencoder, aligns MRI features to the CT space via contrastive objectives, and performs attention-guided residual correction. In paired same-day scans (<i>N</i> = 70; <sup>18</sup>F-FDG, <sup>18</sup>F-florbetaben, and <sup>18</sup>F-florzolotau), cross-platform bias fell by &gt;80% while preserving inter-regional biological topology. The framework generalized zero-shot to held-out tracers (<sup>18</sup>F-florbetapir and <sup>18</sup>F-FP-CIT) without retraining. Multicenter validation (<i>N</i> = 420; three sites, four vendors) reduced amyloid Centiloid discrepancies from 23.6 to 4.1 (close to, though slightly above, PET-CT test–retest variability) and aligned tau SUVR thresholds. These results support more consistent cross-platform diagnostic cut-offs and reliable longitudinal monitoring when patients transition between modalities, establishing a practical route to scalable, radiation-sparing quantitative PET in therapeutic workflows.</p>

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A unified deep learning framework for cross-platform harmonization of multi-tracer PET quantification in neurodegenerative disease

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月30日

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  • Jing Wang,
  • Aocheng Zhong,
  • Qian Xu,
  • Haolin Huang,
  • Yuhua Zhu,
  • Jiaying Lu,
  • Min Wang,
  • Jiehui Jiang,
  • Chengyang Li,
  • Ming Ni,
  • Kaicong Sun,
  • Yihui Guan,
  • Jie Lu,
  • Mei Tian,
  • Dinggang Shen,
  • Huiwei Zhang,
  • Qian Wang,
  • Chuantao Zuo

摘要

Quantitative PET underpins diagnosis and treatment monitoring in neurodegenerative disease, yet systematic biases between PET-MRI and PET-CT preclude threshold transfer and cross-site comparability. We developed and validated the first unified, anatomically guided deep-learning framework to harmonize PET-MRI quantification to PET-CT standards across multiple tracers and scanner manufacturers. The model learns CT-anchored attenuation representations using a vision transformer autoencoder, aligns MRI features to the CT space via contrastive objectives, and performs attention-guided residual correction. In paired same-day scans (N = 70; 18F-FDG, 18F-florbetaben, and 18F-florzolotau), cross-platform bias fell by >80% while preserving inter-regional biological topology. The framework generalized zero-shot to held-out tracers (18F-florbetapir and 18F-FP-CIT) without retraining. Multicenter validation (N = 420; three sites, four vendors) reduced amyloid Centiloid discrepancies from 23.6 to 4.1 (close to, though slightly above, PET-CT test–retest variability) and aligned tau SUVR thresholds. These results support more consistent cross-platform diagnostic cut-offs and reliable longitudinal monitoring when patients transition between modalities, establishing a practical route to scalable, radiation-sparing quantitative PET in therapeutic workflows.