6 citations · 20 across the 11 of their papers we have counts for
11 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…
Simultaneous Deep Learning of Myocardium Segmentation and T2 Quantification for Acute Myocardial Infarction MRI
Yirong Zhou, Chengyan Wang, Mengtian Lu +17
In cardiac Magnetic Resonance Imaging (MRI) analysis, simultaneous myocardial segmentation and T2 quantification are crucial for assessing myocardial pathologies. Existing methods…
Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI
Zi Wang, Min Xiao, Yirong Zhou +13
Dynamic magnetic resonance imaging (MRI) plays an indispensable role in cardiac diagnosis. To enable fast imaging, the k-space data can be undersampled but the image reconstruction…
Cloud-Magnetic Resonance Imaging System: In the Era of 6G and Artificial Intelligence
Yirong Zhou, Yanhuang Wu, Yuhan Su +5
Magnetic Resonance Imaging (MRI) plays an important role in medical diagnosis, generating petabytes of image data annually in large hospitals. This voluminous data stream requires…
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…
One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction
Zi Wang, Xiaotong Yu, Chengyan Wang +25
Magnetic resonance imaging (MRI) is a widely used radiological modality renowned for its radiation-free, comprehensive insights into the human body, facilitating medical diagnoses.…
CloudBrain-MRS: An Intelligent Cloud Computing Platform for in vivo Magnetic Resonance Spectroscopy Preprocessing, Quantification, and Analysis
Xiaodie Chen, Jiayu Li, Dicheng Chen +13
Magnetic resonance spectroscopy (MRS) is an important clinical imaging method for diagnosis of diseases. MRS spectrum is used to observe the signal intensity of metabolites or furt…
CloudBrain-ReconAI: An Online Platform for MRI Reconstruction and Image Quality Evaluation
Yirong Zhou, Chen Qian, Jiayu Li +11
Efficient collaboration between engineers and radiologists is important for image reconstruction algorithm development and image quality evaluation in magnetic resonance imaging (M…
A Faithful Deep Sensitivity Estimation for Accelerated Magnetic Resonance Imaging
Zi Wang, Haoming Fang, Chen Qian +8
Magnetic resonance imaging (MRI) is an essential diagnostic tool that suffers from prolonged scan time. To alleviate this limitation, advanced fast MRI technology attracts extensiv…
Physics-informed Deep Diffusion MRI Reconstruction with Synthetic Data: Break Training Data Bottleneck in Artificial Intelligence
Chen Qian, Haoyu Zhang, Yuncheng Gao +23
Diffusion magnetic resonance imaging (MRI) is the only imaging modality for non-invasive movement detection of in vivo water molecules, with significant clinical and research appli…