6 citations · 9 across the 34 of their papers we have counts for
20 papers · 1 filter
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…
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…
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…
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…
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…
st-DTPM: Spatial-Temporal Guided Diffusion Transformer Probabilistic Model for Delayed Scan PET Image Prediction
Ran Hong, Yuxia Huang, Lei Liu +4
PET imaging is widely employed for observing biological metabolic activities within the human body. However, numerous benign conditions can cause increased uptake of radiopharmaceu…
Dynamic PET Image Prediction Using a Network Combining Reversible and Irreversible Modules
Jie Sun, Junyan Zhang, Qian Xia +6
Dynamic positron emission tomography (PET) images can reveal the distribution of tracers in the organism and the dynamic processes involved in biochemical reactions, and it is wide…
TC-KANRecon: High-Quality and Accelerated MRI Reconstruction via Adaptive KAN Mechanisms and Intelligent Feature Scaling
Ruiquan Ge, Xiao Yu, Yifei Chen +10
Magnetic Resonance Imaging (MRI) has become essential in clinical diagnosis due to its high resolution and multiple contrast mechanisms. However, the relatively long acquisition ti…
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-…
Synthetic CT Generation via Variant Invertible Network for All-digital Brain PET Attenuation Correction
Yu Guan, Bohui Shen, Xinchong Shi +3
Attenuation correction (AC) is essential for the generation of artifact-free and quantitatively accurate positron emission tomography (PET) images. However, AC of PET faces challen…
One Sample Diffusion Model in Projection Domain for Low-Dose CT Imaging
Bin Huang, Liu Zhang, Shiyu Lu +3
Low-dose computed tomography (CT) plays a significant role in reducing the radiation risk in clinical applications. However, lowering the radiation dose will significantly degrade…
Iterative Reconstruction for Low-Dose CT using Deep Gradient Priors of Generative Model
Zhuonan He, Yikun Zhang, Yu Guan +4
Dose reduction in computed tomography (CT) is essential for decreasing radiation risk in clinical applications. Iterative reconstruction is one of the most promising ways to compen…