15 citations · 37 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
Joint multi-contrast Variational Network reconstruction (jVN) with application to rapid 2D and 3D imaging
Daniel Polak, Stephen Cauley, Berkin Bilgic +4
Purpose: To improve the image quality of highly accelerated multi-channel MRI data by learning a joint variational network that reconstructs multiple clinical contrasts jointly. Me…
Nonlinear Dipole Inversion (NDI) enables Quantitative Susceptibility Mapping (QSM) without parameter tuning
Daniel Polak, Itthi Chatnuntawech, Jaeyeon Yoon +6
We propose Nonlinear Dipole Inversion (NDI) for high-quality Quantitative Susceptibility Mapping (QSM) without regularization tuning, while matching the image quality of state-of-t…
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…