3 citations · 15 across the 11 of their papers we have counts for
9 papers
LINEAR: Learning Implicit Neural Representation With Explicit Physical Priors for Accelerated Quantitative T1rho Mapping
Yuanyuan Liu, Jinwen Xie, Zhuo-Xu Cui +4
Quantitative T1rho mapping has shown promise in clinical and research studies. However, it suffers from long scan times. Deep learning-based techniques have been successfully appli…
A Numerical Truncation Approximation with A Posteriori Error Bounds for the Solution of Poisson's Equation
Saied Mahdian, Peter W. Glynn, Yuanyuan Liu
The solution to Poisson's equation arise in many Markov chain and Markov jump process settings, including that of the central limit theorem, value functions for average reward Mark…
A Two-Stage Generative Model with CycleGAN and Joint Diffusion for MRI-based Brain Tumor Detection
Wenxin Wang, Zhuo-Xu Cui, Guanxun Cheng +7
Accurate detection and segmentation of brain tumors is critical for medical diagnosis. However, current supervised learning methods require extensively annotated images and the sta…
Matrix Completion-Informed Deep Unfolded Equilibrium Models for Self-Supervised k-Space Interpolation in MRI
Chen Luo, Huayu Wang, Taofeng Xie +4
Recently, regularization model-driven deep learning (DL) has gained significant attention due to its ability to leverage the potent representational capabilities of DL while retain…
Physics-Informed DeepMRI: Bridging the Gap from Heat Diffusion to k-Space Interpolation
Zhuo-Xu Cui, Congcong Liu, Xiaohong Fan +11
In the field of parallel imaging (PI), alongside image-domain regularization methods, substantial research has been dedicated to exploring -space interpolation. However, the int…
A Hierarchical Destroy and Repair Approach for Solving Very Large-Scale Travelling Salesman Problem
Zhang-Hua Fu, Sipeng Sun, Jintong Ren +6
For prohibitively large-scale Travelling Salesman Problems (TSPs), existing algorithms face big challenges in terms of both computational efficiency and solution quality. To addres…