Deep Adaptive Transfer Learning for Site-Specific PET Attenuation and Scatter Correction from Multi-National/Institutional Datasets

Attenuation and scatter correction (ASC) must be performed for quantitative PET imaging. ASC is a challenging task in PET-only and PET/MRI systems. Different single-center or scanner-specific studies have been performed by using deep learning (DL) algorithms. However, the generalizability of these models is limited. In addition, providing large datasets for data-hungry DL models is a bottleneck. However, a universal model may not perform very well for each center because of high variability across different centers in scanners and image acquisition and reconstruction protocols. Therefore, we aimed to apply deep transfer learning for site-specific PET ASC utilizing a multi-center dataset. Altogether, 430
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Ga-PSMA/DOTA PET/CT images from three countries (Switzerland, Iran, and Canada), including eight different centers, were enrolled in this study. In all centers, PET images were corrected by using CT images for ASC. In addition, we implemented a deep supervised U-Net network architecture, U2Net, as the core DL model. Different scenarios were investigated in this study, including (i) training and testing models for each center separately: center-based (CeBa); (ii) training and testing models using the entirety of the dataset: centralized (CeZe); i.e., entire data pooled together; and (iii) transfer learning (TrLe) where training was performed using pooled data to one server using entire dataset and then TrLe were performed for each center separately to build site-specific models for each center. In terms of absolute relative error (ARE%), CeBa, CeZe, and TrLe achieved 36 ± 13 (CI95%: 33 to 39), 31 ± 34 (CI95%: 24 to 38).Furthermore, using TrLe outperformed the centralized and center-based models in terms of accurate ASC image generation.