110 citations · 126 across the 7 of their papers we have counts for
7 papers
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…
Multi-scale Neural ODEs for 3D Medical Image Registration
Junshen Xu, Eric Z. Chen, Xiao Chen +2
Image registration plays an important role in medical image analysis. Conventional optimization based methods provide an accurate estimation due to the iterative process at the cos…
Enhanced detection of fetal pose in 3D MRI by Deep Reinforcement Learning with physical structure priors on anatomy
Molin Zhang, Junshen Xu, Esra Abaci Turk +3
Fetal MRI is heavily constrained by unpredictable and substantial fetal motion that causes image artifacts and limits the set of viable diagnostic image contrasts. Current mitigati…
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…