Generalizable CT-free PET attenuation and scatter correction via few-shot cross domain adaptation.
Authors
Affiliations (16)
Affiliations (16)
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiao Tong University, Shanghai, China.
- Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
- School of Computer Science, University of Nottingham Ningbo China, Ningbo, China.
- School of Computer Science, University of Nottingham, Nottingham, UK.
- Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, State Key Laboratory of Biomedical Imaging Science and System, Shenzhen, China.
- Faculty of Applied Science, Macao Polytechnic University, Macao, China.
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
- Department of Nuclear Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
- Department of PET/CT, The First Affiliated Hospital of Shandong First Medical University, Jinan, China.
- Department of Nuclear Medicine, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University, The Affiliated Hospital of Beijing Institute of Technology), Zhuhai, China.
- School of Computer Science, University of Nottingham Ningbo China, Ningbo, China. [email protected].
- Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. [email protected].
- Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, State Key Laboratory of Biomedical Imaging Science and System, Shenzhen, China. [email protected].
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. [email protected].
- Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiao Tong University, Shanghai, China. [email protected].
Abstract
The rapid advancements in PET technology, coupled with the need for accurate and efficient imaging, necessitate the development of robust and generalizable methods for CT-free attenuation and scatter correction (ASC). Deep learning offers a promising solution, but exhibits limited performance when tested in diverse clinical settings and varying imaging conditions. We propose a few-shot fine-tuning paradigm that enables efficient adaptation of models from a source domain to a new target domain. Our backbone network incorporates statistical modulation to extract domain-specific distribution information and employs pixel-wise factor scaling modeling to disentangle ASC factor maps from input images. On a large and diverse dataset of 1539 subjects across multiple tracers, scanners, and centers, we evaluate model performance under single-tracer training, multi-tracer joint training, and few-shot adaptation strategies. Although joint training demonstrates strong performance on known tracers, the proposed few-shot adaptation approach, CrossPET-Adapt, excels at adapting to unseen domains with minimal data, outperforming joint training. This method significantly reduces radiation exposure and data requirements, offering a rapid and robust solution for CT-free PET ASC in varied clinical environments.