13 citations · 28 across the 16 of their papers we have counts for
16 papers
Simultaneous q-Space Sampling Optimization and Reconstruction for Fast and High-fidelity Diffusion Magnetic Resonance Imaging
Jing Yang, Jian Cheng, Cheng Li +4
Diffusion Magnetic Resonance Imaging (dMRI) plays a crucial role in the noninvasive investigation of tissue microstructural properties and structural connectivity in the \textit{in…
Super resolution dual-layer CBCT imaging with model-guided deep learning
Jiongtao Zhu, Ting Su, Xin Zhang +6
Objective: This study aims at investigating a novel super resolution CBCT imaging technique with the dual-layer flat panel detector (DL-FPD). Approach: In DL-FPD based CBCT imaging…
Self-Supervised Federated Learning for Fast MR Imaging
Juan Zou, Cheng Li, Ruoyou Wu +3
Federated learning (FL) based magnetic resonance (MR) image reconstruction can facilitate learning valuable priors from multi-site institutions without violating patient's privacy…
Model-based Federated Learning for Accurate MR Image Reconstruction from Undersampled k-space Data
Ruoyou Wu, Cheng Li, Juan Zou +3
Deep learning-based methods have achieved encouraging performances in the field of magnetic resonance (MR) image reconstruction. Nevertheless, to properly learn a powerful and robu…
SPIRiT-Diffusion: Self-Consistency Driven Diffusion Model for Accelerated MRI
Zhuo-Xu Cui, Chentao Cao, Yue Wang +6
Diffusion models have emerged as a leading methodology for image generation and have proven successful in the realm of magnetic resonance imaging (MRI) reconstruction. However, exi…
SPIRiT-Diffusion: SPIRiT-driven Score-Based Generative Modeling for Vessel Wall imaging
Chentao Cao, Zhuo-Xu Cui, Jing Cheng +4
Diffusion model is the most advanced method in image generation and has been successfully applied to MRI reconstruction. However, the existing methods do not consider the character…
Iterative Data Refinement for Self-Supervised MR Image Reconstruction
Xue Liu, Juan Zou, Xiawu Zheng +3
Magnetic Resonance Imaging (MRI) has become an important technique in the clinic for the visualization, detection, and diagnosis of various diseases. However, one bottleneck limita…
Semi-Supervised and Self-Supervised Collaborative Learning for Prostate 3D MR Image Segmentation
Yousuf Babiker M. Osman, Cheng Li, Weijian Huang +4
Volumetric magnetic resonance (MR) image segmentation plays an important role in many clinical applications. Deep learning (DL) has recently achieved state-of-the-art or even human…
Uncertainty-Aware Multi-Parametric Magnetic Resonance Image Information Fusion for 3D Object Segmentation
Cheng Li, Yousuf Babiker M. Osman, Weijian Huang +4
Multi-parametric magnetic resonance (MR) imaging is an indispensable tool in the clinic. Consequently, automatic volume-of-interest segmentation based on multi-parametric MR imagin…
SelfCoLearn: Self-supervised collaborative learning for accelerating dynamic MR imaging
Juan Zou, Cheng Li, Sen Jia +4
Lately, deep learning has been extensively investigated for accelerating dynamic magnetic resonance (MR) imaging, with encouraging progresses achieved. However, without fully sampl…
Multimodal Brain Disease Classification with Functional Interaction Learning from Single fMRI Volume
Wei Dai, Ziyao Zhang, Lixia Tian +4
In neuroimaging analysis, fMRI can well assess the function changes for brain diseases with no obvious structural lesions. To date, most deep-learning-based fMRI studies have emplo…
Rethinking the optimization process for self-supervised model-driven MRI reconstruction
Weijian Huang, Cheng Li, Wenxin Fan +4
Recovering high-quality images from undersampled measurements is critical for accelerated MRI reconstruction. Recently, various supervised deep learning-based MRI reconstruction me…
PARCEL: Physics-based Unsupervised Contrastive Representation Learning for Multi-coil MR Imaging
Shanshan Wang, Ruoyou Wu, Cheng Li +5
With the successful application of deep learning to magnetic resonance (MR) imaging, parallel imaging techniques based on neural networks have attracted wide attention. However, in…
Expert Knowledge-guided Geometric Representation Learning for Magnetic Resonance Imaging-based Glioma Grading
Yeqi Wang, Longfei Li, Cheng Li +4
Radiomics and deep learning have shown high popularity in automatic glioma grading. Radiomics can extract hand-crafted features that quantitatively describe the expert knowledge of…
Radiomic biomarker extracted from PI-RADS 3 patients support more eìcient and robust prostate cancer diagnosis: a multi-center study
Longfei Li, Rui Yang, Xin Chen +5
Prostate Imaging Reporting and Data System (PI-RADS) based on multi-parametric MRI classiêes patients into 5 categories (PI-RADS 1-5) for routine clinical diagnosis guidance. Howev…
Discriminating Quantum States with Quantum Machine Learning
David Quiroga, Prasanna Date, Raphael C. Pooser
Quantum machine learning (QML) algorithms have obtained great relevance in the machine learning (ML) field due to the promise of quantum speedups when performing basic linear algeb…