12 papers
Physics-Driven Independent Pair Generation for Iterative Self-Supervised Low-Dose CT Denoising
Xianlei Han, Shaoyu Wang, Jiancheng Fang +2
Low-dose computed tomography (LDCT) measurements contain mixed Poisson-Gaussian noise. However, most self-supervised methods rely on generic image statistics and do not explicitly…
Shared-Structure 4D Spectral Gaussian Representation for Sparse-View Spectral CT Reconstruction
Jiancheng Fang, Shaoyu Wang, Wenjun Xia +2
Sparse-view spectral computed tomography (CT) reconstructs energy-resolved attenuation volumes from limited projection views, requiring simultaneous handling of angular undersampli…
Residual Gaussian Splatting for Ultra Sparse-View CBCT Reconstruction
Jian Lin, Jiancheng Fang, Shaoyu Wang +4
While 3D Gaussian splatting (3DGS) offers explicit and efficient scene representations for cone-beam computed tomography reconstruction, conventional photometric optimization inher…
SCOUT: Fast Spectral CT Imaging in Ultra LOw-data Regimes via PseUdo-label GeneraTion
Guoquan Wei, Liu Shi, Shaoyu Wang +3
Noise and artifacts during computed tomography (CT) scans are a fundamental challenge affecting disease diagnosis. However, current methods either involve excessively long reconstr…
Continuity-driven Synergistic Diffusion with Neural Priors for Ultra-Sparse-View CBCT Reconstruction
Junlin Wang, Jiancheng Fang, Peng Peng +2
The clinical application of cone-beam computed tomography (CBCT) is constrained by the inherent trade-off between radiation exposure and image quality. Ultra-sparse angular samplin…
FSP-Diff: Full-Spectrum Prior-Enhanced DualDomain Latent Diffusion for Ultra-Low-Dose Spectral CT Reconstruction
Peng Peng, Xinrui Zhang, Junlin Wang +3
Spectral computed tomography (CT) with photon-counting detectors holds immense potential for material discrimination and tissue characterization. However, under ultra-low-dose cond…
Iterative Diffusion-Refined Neural Attenuation Fields for Multi-Source Stationary CT Reconstruction: NAF Meets Diffusion Model
Jiancheng Fang, Shaoyu Wang, Junlin Wang +3
Multi-source stationary computed tomography (CT) has recently attracted attention for its ability to achieve rapid image reconstruction, making it suitable for time-sensitive clini…
UniSino: Physics-Driven Foundational Model for Universal CT Sinogram Standardization
Xingyu Ai, Shaoyu Wang, Zhiyuan Jia +4
During raw-data acquisition in CT imaging, diverse factors can degrade the collected sinograms, with undersampling and noise leading to severe artifacts and noise in reconstructed…
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…
Virtual-mask Informed Prior for Sparse-view Dual-Energy CT Reconstruction
Zini Chen, Yao Xiao, Junyan Zhang +3
Sparse-view sampling in dual-energy computed tomography (DECT) significantly reduces radiation dose and increases imaging speed, yet is highly prone to artifacts. Although diffusio…
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…
Zero-shot Dynamic MRI Reconstruction with Global-to-local Diffusion Model
Yu Guan, Kunlong Zhang, Qi Qi +5
Diffusion models have recently demonstrated considerable advancement in the generation and reconstruction of magnetic resonance imaging (MRI) data. These models exhibit great poten…