Reducing scan time in Positron Emission Tomography (PET) imaging while maintaining high-quality images is crucial for minimizing patient discomfort and radiation exposure. Due to the limited size of datasets and distribution discrepancy across scanners in medical imaging, fine-tuning in a parameter-efficient and effective manner is on the rise. Motivated by the potential of Parameter-Efficient Fine-Tuning (PEFT), we aim to address these issues by effectively leveraging PEFT to improve limited data and GPU resource issues in multi-scanner setups. In this paper, we introduce PETITE, Parameter-Efficient Fine-Tuning for MultI-scanner PET to PET REconstruction that uses fewer than 1% of the parameters. To the best of our knowledge, this study is the first to systematically explore the efficacy of diverse PEFT techniques in medical imaging reconstruction tasks via prevalent encoder-decoder-type deep models. This investigation, in particular, brings intriguing insights into PETITE as we show further improvements by treating encoder and decoder separately and mixing different PEFT methods, namely, Mix-PEFT. Using multi-scanner PET datasets comprised of five different scanners, we extensively test the cross-scanner PET scan time reduction performances (i.e., a model pre-trained on one scanner is fine-tuned on a different scanner) of 21 feasible Mix-PEFT combinations to derive optimal PETITE. We show that training with less than 1% parameters using PETITE performs on par with full fine-tuning (i.e., 100% parameter)

翻译：在正电子发射断层扫描（PET）成像中，在保持高质量图像的同时减少扫描时间，对于最大限度地减少患者不适和辐射暴露至关重要。由于医学成像中数据集规模有限以及不同扫描仪之间的分布差异，以参数高效且有效的方式进行微调正日益兴起。受参数高效微调（PEFT）潜力的启发，我们旨在通过有效利用PEFT来解决多扫描仪设置中有限数据和GPU资源的问题。本文中，我们介绍了PETITE——一种用于多扫描仪PET到PET重建的参数高效微调方法，其使用的参数量少于总参数的1%。据我们所知，本研究首次通过流行的编码器-解码器型深度模型，系统性地探索了多种PEFT技术在医学图像重建任务中的效能。这项研究尤其为PETITE带来了引人入胜的见解，因为我们通过分别处理编码器和解码器以及混合不同的PEFT方法（即Mix-PEFT）展示了进一步的改进。利用由五台不同扫描仪组成的多扫描仪PET数据集，我们广泛测试了21种可行的Mix-PEFT组合在跨扫描仪PET扫描时间减少性能（即，在一个扫描仪上预训练的模型在另一个不同扫描仪上进行微调）方面的表现，以推导出最优的PETITE配置。我们证明，使用PETITE以少于1%的参数进行训练，其性能可与全参数微调（即100%参数）相媲美。