7 papers
ALL-PET: A Low-resource and Low-shot PET Foundation Model in Projection Domain
Bin Huang, Kang Chen, Bingxuan Li +2
Building large-scale foundation model for PET imaging is hindered by limited access to labeled data and insufficient computational resources. To overcome data scarcity and efficien…
PET Tracer Separation Using Conditional Diffusion Transformer with Multi-latent Space Learning
Bin Huang, Feihong Xu, Xinchong Shi +4
In clinical practice, single-radiotracer positron emission tomography (PET) is commonly used for imaging. Although multi-tracer PET imaging can provide supplementary information of…
PRO: Projection Domain Synthesis for CT Imaging
Kang Chen, Bin Huang, Xuebin Yang +3
Synthetic CT projection data is crucial for advancing imaging research, yet its generation remains challenging. Current image domain methods are limited as they cannot simulate the…
Ordered-subsets Multi-diffusion Model for Sparse-view CT Reconstruction
Pengfei Yu, Bin Huang, Minghui Zhang +3
Score-based diffusion models have shown significant promise in the field of sparse-view CT reconstruction. However, the projection dataset is large and riddled with redundancy. Con…
A Continual Learning-driven Model for Accurate and Generalizable Segmentation of Clinically Comprehensive and Fine-grained Whole-body Anatomies in CT
Dazhou Guo, Zhanghexuan Ji, Yanzhou Su +31
Precision medicine in the quantitative management of chronic diseases and oncology would be greatly improved if the Computed Tomography (CT) scan of any patient could be segmented,…
Diffusion Transformer Meets Random Masks: An Advanced PET Reconstruction Framework
Bin Huang, Binzhong He, Yanhan Chen +4
Deep learning has significantly advanced PET image re-construction, achieving remarkable improvements in image quality through direct training on sinogram or image data. Traditiona…