6 citations · 9 across the 34 of their papers we have counts for
35 papers
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…
GR-Diffusion: 3D Gaussian Representation Meets Diffusion in Whole-Body PET Reconstruction
Mengxiao Geng, Zijie Chen, Ran Hong +2
Positron emission tomography (PET) reconstruction is a critical challenge in molecular imaging, often hampered by noise amplification, structural blurring, and detail loss due to s…
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…
AS-Mamba: Asymmetric Self-Guided Mamba Decoupled Iterative Network for Metal Artifact Reduction
Bowen Ning, Zekun Zhou, Xinyi Zhong +5
Metal artifact significantly degrades Computed Tomography (CT) image quality, impeding accurate clinical diagnosis. However, existing deep learning approaches, such as CNN and Tran…
Visible Singularities Guided Correlation Network for Limited-Angle CT Reconstruction
Yiyang Wen, Liu Shi, Zekun Zhou +2
Limited-angle computed tomography (LACT) offers the advantages of reduced radiation dose and shortened scanning time. Traditional reconstruction algorithms exhibit various inherent…
Anatomy Aware Cascade Network: Bridging Epistemic Uncertainty and Geometric Manifold for 3D Tooth Segmentation
Bing Yu, Liu Shi, Haitao Wang +4
Accurate three-dimensional (3D) tooth segmentation from Cone-Beam Computed Tomography (CBCT) is a prerequisite for digital dental workflows. However, achieving high-fidelity segmen…
LaminoDiff: Generative Computed Laminography via Near-Isotropic Spectral Supervision and Anisotropic Geometry
Tan Liu, Liu Shi, Binghuang Peng +4
Computed Laminography (CL) is widely used for nondestructive inspection of extended planar objects, but its restricted angular coverage produces an anisotropic point spread functio…
Meta-information Guided Cross-domain Synergistic Diffusion Model for Low-dose PET Reconstruction
Mengxiao Geng, Ran Hong, Xiaoling Xu +2
Low-dose PET imaging is crucial for reducing patient radiation exposure but faces challenges like noise interference, reduced contrast, and difficulty in preserving physiological d…
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…
Physics-Guided Null-Space Diffusion with Sparse Masking for Corrective Sparse-View CT Reconstruction
Zekun Zhou, Yanru Gong, Liu Shi +1
Diffusion models have demonstrated remarkable generative capabilities in image processing tasks. We propose a Sparse condition Temporal Rewighted Integrated Distribution Estimation…
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…
Extendable Generalization Self-Supervised Diffusion for Low-Dose CT Reconstruction
Guoquan Wei, Liu Shi, Zekun Zhou +4
Current methods based on deep learning for self-supervised low-dose CT (LDCT) reconstruction, while reducing the dependence on paired data, face the problem of significantly decrea…
Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction
Mengxiao Geng, Ran Hong, Bingxuan Li +1
Ultra-low-dose positron emission tomography (PET) reconstruction holds significant potential for reducing patient radiation exposure and shortening examination times. However, it m…
PWD: Prior-Guided and Wavelet-Enhanced Diffusion Model for Limited-Angle CT
Yi Liu, Yiyang Wen, Zekun Zhou +5
Generative diffusion models have received increasing attention in medical imaging, particularly in limited-angle computed tomography (LACT). Standard diffusion models achieve high-…
FD-DiT: Frequency Domain-Directed Diffusion Transformer for Low-Dose CT Reconstruction
Qiqing Liu, Guoquan Wei, Zekun Zhou +3
Low-dose computed tomography (LDCT) reduces radiation exposure but suffers from image artifacts and loss of detail due to quantum and electronic noise, potentially impacting diagno…
Adaptive Mask-guided K-space Diffusion for Accelerated MRI Reconstruction
Qinrong Cai, Yu Guan, Zhibo Chen +3
As the deep learning revolution marches on, masked modeling has emerged as a distinctive approach that involves predicting parts of the original data that are proportionally masked…
A Prior-Guided Joint Diffusion Model in Projection Domain for PET Tracer Conversion
Fang Chen, Weifeng Zhang, Xingyu Ai +3
Positron emission tomography (PET) is widely used to assess metabolic activity, but its application is limited by the availability of radiotracers. 18F-labeled fluorodeoxyglucose (…
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…
ESDiff: Encoding Strategy-inspired Diffusion Model with Few-shot Learning for Color Image Inpainting
Junyan Zhang, Yan Li, Mengxiao Geng +2
Image inpainting is a technique used to restore missing or damaged regions of an image. Traditional methods primarily utilize information from adjacent pixels for reconstructing mi…
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…
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…
Physics-informed DeepCT: Sinogram Wavelet Decomposition Meets Masked Diffusion
Zekun Zhou, Tan Liu, Bing Yu +3
Diffusion model shows remarkable potential on sparse-view computed tomography (SVCT) reconstruction. However, when a network is trained on a limited sample space, its generalizatio…