110 citations · 128 across the 10 of their papers we have counts for
6 papers · 1 filter
Zero-Shot Self-Supervised Joint Temporal Image and Sensitivity Map Reconstruction via Linear Latent Space
Molin Zhang, Junshen Xu, Yamin Arefeen +1
Fast spin-echo (FSE) pulse sequences for Magnetic Resonance Imaging (MRI) offer important imaging contrast in clinically feasible scan times. T2-shuffling is widely used to resolve…
SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRI
Junshen Xu, Daniel Moyer, P. Ellen Grant +3
Volumetric reconstruction of fetal brains from multiple stacks of MR slices, acquired in the presence of almost unpredictable and often severe subject motion, is a challenging task…
STRESS: Super-Resolution for Dynamic Fetal MRI using Self-Supervised Learning
Junshen Xu, Esra Abaci Turk, P. Ellen Grant +2
Fetal motion is unpredictable and rapid on the scale of conventional MR scan times. Therefore, dynamic fetal MRI, which aims at capturing fetal motion and dynamics of fetal functio…
Deformed2Self: Self-Supervised Denoising for Dynamic Medical Imaging
Junshen Xu, Elfar Adalsteinsson
Image denoising is of great importance for medical imaging system, since it can improve image quality for disease diagnosis and downstream image analyses. In a variety of applicati…
Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI consistency
Junshen Xu, Sayeri Lala, Borjan Gagoski +4
Fetal brain MRI is useful for diagnosing brain abnormalities but is challenged by fetal motion. The current protocol for T2-weighted fetal brain MRI is not robust to motion so imag…
Fetal Pose Estimation in Volumetric MRI using a 3D Convolution Neural Network
Junshen Xu, Molin Zhang, Esra Abaci Turk +5
The performance and diagnostic utility of magnetic resonance imaging (MRI) in pregnancy is fundamentally constrained by fetal motion. Motion of the fetus, which is unpredictable an…