9 citations · 12 across the 19 of their papers we have counts for
19 papers
Zero-shot Low-Field MRI Enhancement via Diffusion-Based Adaptive Contrast Transport
Muyu Liu, Chenhe Du, Xuanyu Tian +5
Low-field (LF) magnetic resonance imaging (MRI) democratizes access to diagnostic imaging but is fundamentally limited by low signal-to-noise ratio and significant tissue contrast…
Resolving Blind Inverse Problems under Dynamic Range Compression via Structured Forward Operator Modeling
Muyu Liu, Xuanyu Tian, Chenhe Du +3
Recovering radiometric fidelity from unknown dynamic range compression (UDRC), such as low-light enhancement and HDR reconstruction, is a challenging blind inverse problem, due to…
Improving 2D Diffusion Models for 3D Medical Imaging with Inter-Slice Consistent Stochasticity
Chenhe Du, Qing Wu, Xuanyu Tian +3
3D medical imaging is in high demand and essential for clinical diagnosis and scientific research. Currently, diffusion models (DMs) have become an effective tool for medical imagi…
Plug-and-Play Diffusion Meets ADMM: Dual-Variable Coupling for Robust Medical Image Reconstruction
Chenhe Du, Xuanyu Tian, Qing Wu +4
Plug-and-Play diffusion prior (PnPDP) frameworks have emerged as a powerful paradigm for solving imaging inverse problems by treating pretrained generative models as modular priors…
NICE: Neural Implicit Craniofacial Model for Orthognathic Surgery Prediction
Jiawen Yang, Yihui Cao, Xuanyu Tian +2
Orthognathic surgery is a crucial intervention for correcting dentofacial skeletal deformities to enhance occlusal functionality and facial aesthetics. Accurate postoperative facia…
Unsupervised Motion-Compensated Decomposition for Cardiac MRI Reconstruction via Neural Representation
Xuanyu Tian, Lixuan Chen, Qing Wu +4
Cardiac magnetic resonance (CMR) imaging is widely used to characterize cardiac morphology and function. To accelerate CMR imaging, various methods have been proposed to recover hi…
Low-Rank Augmented Implicit Neural Representation for Unsupervised High-Dimensional Quantitative MRI Reconstruction
Haonan Zhang, Guoyan Lao, Yuyao Zhang +1
Quantitative magnetic resonance imaging (qMRI) provides tissue-specific parameters vital for clinical diagnosis. Although simultaneous multi-parametric qMRI (MP-qMRI) technologies…
Super-temporal-resolution Photoacoustic Imaging with Dynamic Reconstruction through Implicit Neural Representation in Sparse-view
Youshen Xiao, Yiling Shi, Ruixi Sun +3
Dynamic Photoacoustic Computed Tomography (PACT) is an important imaging technique for monitoring physiological processes, capable of providing high-contrast images of optical abso…
SUFFICIENT: A scan-specific unsupervised deep learning framework for high-resolution 3D isotropic fetal brain MRI reconstruction
Jiangjie Wu, Lixuan Chen, Zhenghao Li +6
High-quality 3D fetal brain MRI reconstruction from motion-corrupted 2D slices is crucial for clinical diagnosis. Reliable slice-to-volume registration (SVR)-based motion correctio…
JSolver: Joint Spectrum Estimation and Multi-Material Decomposition from Single-Energy CT Projections
Qing Wu, Hongjiang Wei, Jingyi Yu +2
Multi-material decomposition (MMD) enables quantitative reconstruction of tissue compositions in the human body, supporting a wide range of clinical applications. However, traditio…
Unsupervised Self-Prior Embedding Neural Representation for Iterative Sparse-View CT Reconstruction
Xuanyu Tian, Lixuan Chen, Qing Wu +4
Emerging unsupervised implicit neural representation (INR) methods, such as NeRP, NeAT, and SCOPE, have shown great potential to address sparse-view computed tomography (SVCT) inve…
Unsupervised Multi-Parameter Inverse Solving for Reducing Ring Artifacts in 3D X-Ray CBCT
Qing Wu, Hongjiang Wei, Jingyi Yu +1
Ring artifacts are prevalent in 3D cone-beam computed tomography (CBCT) due to non-ideal responses of X-ray detectors, substantially affecting image quality and diagnostic reliabil…
Coordinate-Based Neural Representation Enabling Zero-Shot Learning for 3D Multiparametric Quantitative MRI
Guoyan Lao, Ruimin Feng, Haikun Qi +5
Quantitative magnetic resonance imaging (qMRI) offers tissue-specific physical parameters with significant potential for neuroscience research and clinical practice. However, lengt…
Moner: Motion Correction in Undersampled Radial MRI with Unsupervised Neural Representation
Qing Wu, Chenhe Du, Xuanyu Tian +3
Motion correction (MoCo) in radial MRI is a particularly challenging problem due to the unpredictability of subject movement. Current state-of-the-art (SOTA) MoCo algorithms often…
Highly Accelerated MRI via Implicit Neural Representation Guided Posterior Sampling of Diffusion Models
Jiayue Chu, Chenhe Du, Xiyue Lin +2
Reconstructing high-fidelity magnetic resonance (MR) images from under-sampled k-space is a commonly used strategy to reduce scan time. The posterior sampling of diffusion models b…
Zero-Shot Image Denoising for High-Resolution Electron Microscopy
Xuanyu Tian, Zhuoya Dong, Xiyue Lin +5
High-resolution electron microscopy (HREM) imaging technique is a powerful tool for directly visualizing a broad range of materials in real-space. However, it faces challenges in d…
DPER: Diffusion Prior Driven Neural Representation for Limited Angle and Sparse View CT Reconstruction
Chenhe Du, Xiyue Lin, Qing Wu +9
Limited-angle and sparse-view computed tomography (LACT and SVCT) are crucial for expanding the scope of X-ray CT applications. However, they face challenges due to incomplete data…
IMJENSE: Scan-specific Implicit Representation for Joint Coil Sensitivity and Image Estimation in Parallel MRI
Ruimin Feng, Qing Wu, Jie Feng +4
Parallel imaging is a commonly used technique to accelerate magnetic resonance imaging (MRI) data acquisition. Mathematically, parallel MRI reconstruction can be formulated as an i…
JSMoCo: Joint Coil Sensitivity and Motion Correction in Parallel MRI with a Self-Calibrating Score-Based Diffusion Model
Lixuan Chen, Xuanyu Tian, Jiangjie Wu +4
Magnetic Resonance Imaging (MRI) stands as a powerful modality in clinical diagnosis. However, it is known that MRI faces challenges such as long acquisition time and vulnerability…