6 papers
Cardiac Mesh Flow: One-Step Generation of 3D+t Cardiac Four-Chamber Meshes via Flow Matching
Qiang Ma, Qingjie Meng, Mengyun Qiao +3
Spatio-temporal (3D+t) generative modelling of cardiac shape and motion is crucial for understanding heart structure and function at population scale. Existing generative models fo…
AdamFlow: Adam-based Wasserstein Gradient Flows for Surface Registration in Medical Imaging
Qiang Ma, Qingjie Meng, Xin Hu +2
Surface registration plays an important role for anatomical shape analysis in medical imaging. Existing surface registration methods often face a trade-off between efficiency and r…
Learning a dynamic four-chamber shape model of the human heart for 95,695 UK Biobank participants
Qiang Ma, Qingjie Meng, Yicheng Wu +6
The human heart is a sophisticated system composed of four cardiac chambers with distinct shapes, which function in a coordinated manner. Existing shape models of the heart mainly…
CardiacFlow: 3D+t Four-Chamber Cardiac Shape Completion and Generation via Flow Matching
Qiang Ma, Qingjie Meng, Mengyun Qiao +3
Learning 3D+t shape completion and generation from multi-view cardiac magnetic resonance (CMR) images requires a large amount of high-resolution 3D whole-heart segmentations (WHS)…
MCM: Mamba-based Cardiac Motion Tracking using Sequential Images in MRI
Jiahui Yin, Xinxing Cheng, Jinming Duan +4
Myocardial motion tracking is important for assessing cardiac function and diagnosing cardiovascular diseases, for which cine cardiac magnetic resonance (CMR) has been established…
SACB-Net: Spatial-awareness Convolutions for Medical Image Registration
Xinxing Cheng, Tianyang Zhang, Wenqi Lu +3
Deep learning-based image registration methods have shown state-of-the-art performance and rapid inference speeds. Despite these advances, many existing approaches fall short in ca…