5 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…
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…
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…
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…
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…