2 citations · 3 across the 4 of their papers we have counts for
4 papers
Error Bound Analysis of Physics-Informed Neural Networks-Driven T2 Quantification in Cardiac Magnetic Resonance Imaging
Mengxue Zhang, Qingrui Cai, Yinyin Chen +13
Physics-Informed Neural Networks (PINN) are emerging as a promising approach for quantitative parameter estimation of Magnetic Resonance Imaging (MRI). While existing deep learning…
Robust High-Resolution Multi-Organ Diffusion MRI Using Synthetic-Data-Tuned Prompt Learning
Chen Qian, Haoyu Zhang, Junnan Ma +26
Clinical adoption of multi-shot diffusion-weighted magnetic resonance imaging (multi-shot DWI) for body-wide tumor diagnostics is limited by severe motion-induced phase artifacts f…
Quantitative Analysis of Molecular Transport in the Extracellular Space Using Physics-Informed Neural Network
Jiayi Xie, Hongfeng Li, Jin Cheng +5
The brain extracellular space (ECS), an irregular, extremely tortuous nanoscale space located between cells or between cells and blood vessels, is crucial for nerve cell survival.…
Bloch Equation Enables Physics-informed Neural Network in Parametric Magnetic Resonance Imaging
Qingrui Cai, Liuhong Zhu, Jianjun Zhou +3
Magnetic resonance imaging (MRI) is an important non-invasive imaging method in clinical diagnosis. Beyond the common image structures, parametric imaging can provide the intrinsic…