6 papers · 1 filter
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,…
Partitioned Hankel-based Diffusion Models for Few-shot Low-dose CT Reconstruction
Wenhao Zhang, Bin Huang, Shuyue Chen +3
Low-dose computed tomography (LDCT) plays a vital role in clinical applications by mitigating radiation risks. Nevertheless, reducing radiation doses significantly degrades image q…
MSDiff: Multi-Scale Diffusion Model for Ultra-Sparse View CT Reconstruction
Junyan Zhang, Mengxiao Geng, Pinhuang Tan +4
Computed Tomography (CT) technology reduces radiation haz-ards to the human body through sparse sampling, but fewer sampling angles pose challenges for image reconstruction. Score-…