Abstract:
Deep learning models have shown great potential in reducing low-dose (LD) positron emission tomography (PET) image noise by estimating full-dose (FD) images from the corr...Show MoreMetadata
Abstract:
Deep learning models have shown great potential in reducing low-dose (LD) positron emission tomography (PET) image noise by estimating full-dose (FD) images from the corresponding LD images. Those models, however, when trained on paired LD-FD PET images from a source scanner, fail to generalize well when applied to LD PET images from a target scanner, due to a phenomenon called “domain drift.” In this study, we present a method for cross-scanner LD PET image noise reduction. This is done via a self-ensembling framework using a limited number of paired LD-FD PET images and a large number of LD PET images from the target scanner. The self-ensembling framework leverages the paired 2-D slices from both scanners to learn a regression model. It additionally incorporates a consistency loss on the LD PET images from the target scanner to enhance the model’s generalization capability. We conduct experiments on three datasets, respectively, acquired from three different scanners, including a GE Discovery MI (DMI) scanner, a Siemens Biograph Vision 450 (Vision) scanner, and a UI uMI 780 (uMI) scanner. Results from our comprehensive experiments demonstrate the generalization capability of our method.
Published in: IEEE Transactions on Radiation and Plasma Medical Sciences ( Volume: 8, Issue: 4, April 2024)
Funding Agency:
Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
Department of Nuclear Medicine, Ruijin Hospital and College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Shanxi Medical University, Taiyuan, China
Department of Nuclear Medicine, Ruijin Hospital and College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Shanxi Medical University, Taiyuan, China
Department of Nuclear Medicine, University of Bern, Bern, Switzerland
Department of Nuclear Medicine, Ruijin Hospital and College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Shanxi Medical University, Taiyuan, China
Department of Nuclear Medicine, University of Bern, Bern, Switzerland
Department of Informatics, Technical University of Munich, Munich, Germany
Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
Department of Nuclear Medicine, Ruijin Hospital and College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Shanxi Medical University, Taiyuan, China
Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
Department of Nuclear Medicine, Ruijin Hospital and College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Shanxi Medical University, Taiyuan, China
Department of Nuclear Medicine, Ruijin Hospital and College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Shanxi Medical University, Taiyuan, China
Department of Nuclear Medicine, University of Bern, Bern, Switzerland
Department of Nuclear Medicine, Ruijin Hospital and College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Shanxi Medical University, Taiyuan, China
Department of Nuclear Medicine, University of Bern, Bern, Switzerland
Department of Informatics, Technical University of Munich, Munich, Germany
Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
Department of Nuclear Medicine, Ruijin Hospital and College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Shanxi Medical University, Taiyuan, China