1 citations · 1 across the 10 of their papers we have counts for
11 papers
PLOT-CT: Pre-log Voronoi Decomposition Assisted Generation for Low-dose CT Reconstruction
Bin Huang, Xun Yu, Yikun Zhang +3
Low-dose computed tomography (LDCT) reconstruction is fundamentally challenged by severe noise and compromised data fidelity under reduced radiation exposure. Most existing methods…
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…
RED: Residual Estimation Diffusion for Low-Dose PET Sinogram Reconstruction
Xingyu Ai, Bin Huang, Fang Chen +4
Recent advances in diffusion models have demonstrated exceptional performance in generative tasks across vari-ous fields. In positron emission tomography (PET), the reduction in tr…
Diffusion Transformer Model With Compact Prior for Low-dose PET Reconstruction
Bin Huang, Xubiao Liu, Lei Fang +2
Positron emission tomography (PET) is an advanced medical imaging technique that plays a crucial role in non-invasive clinical diagnosis. However, while reducing radiation exposure…
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-…