1 citations · 1 across the 5 of their papers we have counts for
5 papers
Self-Supervised Slice-to-Volume Reconstruction with Gaussian Representations for Fetal MRI
Yinsong Wang, Thomas Fletcher, Xinzhe Luo +3
Reconstructing 3D fetal MR volumes from motion-corrupted stacks of 2D slices is a crucial and challenging task. Conventional slice-to-volume reconstruction (SVR) methods are time-c…
UPMRI: Unsupervised Parallel MRI Reconstruction via Projected Conditional Flow Matching
Xinzhe Luo, Yingzhen Li, Chen Qin
Reconstructing high-quality images from substantially undersampled k-space data for accelerated MRI presents a challenging ill-posed inverse problem. While supervised deep learning…
Spectral Bias Correction in PINNs for Myocardial Image Registration of Pathological Data
Bastien C. Baluyot, Marta Varela, Chen Qin
Accurate myocardial image registration is essential for cardiac strain analysis and disease diagnosis. However, spectral bias in neural networks impedes modeling high-frequency def…
Unsupervised Accelerated MRI Reconstruction via Ground-Truth-Free Flow Matching
Xinzhe Luo, Yingzhen Li, Chen Qin
Accelerated magnetic resonance imaging involves reconstructing fully sampled images from undersampled k-space measurements. Current state-of-the-art approaches have mainly focused…
Inference Stage Denoising for Undersampled MRI Reconstruction
Yuyang Xue, Chen Qin, Sotirios A. Tsaftaris
Reconstruction of magnetic resonance imaging (MRI) data has been positively affected by deep learning. A key challenge remains: to improve generalisation to distribution shifts bet…