132 citations · 187 across the 10 of their papers we have counts for
10 papers
Motion-Robust Deep Reconstruction for Free-Breathing Cardiac Cine MRI
Mahmut Yurt, Kanghyun Ryu, Zhitao Li +8
Conventional cardiac cine MRI relies on breath-hold Cartesian acquisitions, which are vulnerable to motion artifacts and can be uncomfortable or infeasible, particularly for pediat…
Principled Design of Diffusion-based Optimizers for Inverse Problems
Julio Oscanoa, Irmak Sivgin, Cagan Alkan +4
Score-based diffusion models achieve state-of-the-art performance for inverse problems, but their practical deployment is hindered by long inference times and cumbersome hyperparam…
Scale-Agnostic Super-Resolution in MRI using Feature-Based Coordinate Networks
Dave Van Veen, Rogier van der Sluijs, Batu Ozturkler +9
We propose using a coordinate network decoder for the task of super-resolution in MRI. The continuous signal representation of coordinate networks enables this approach to be scale…
GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction
Batu Ozturkler, Arda Sahiner, Tolga Ergen +6
Unrolled neural networks have recently achieved state-of-the-art accelerated MRI reconstruction. These networks unroll iterative optimization algorithms by alternating between phys…
Scale-Equivariant Unrolled Neural Networks for Data-Efficient Accelerated MRI Reconstruction
Beliz Gunel, Arda Sahiner, Arjun D. Desai +4
Unrolled neural networks have enabled state-of-the-art reconstruction performance and fast inference times for the accelerated magnetic resonance imaging (MRI) reconstruction task.…
VORTEX: Physics-Driven Data Augmentations Using Consistency Training for Robust Accelerated MRI Reconstruction
Arjun D Desai, Beliz Gunel, Batu M Ozturkler +6
Deep neural networks have enabled improved image quality and fast inference times for various inverse problems, including accelerated magnetic resonance imaging (MRI) reconstructio…
Noise2Recon: Enabling Joint MRI Reconstruction and Denoising with Semi-Supervised and Self-Supervised Learning
Arjun D Desai, Batu M Ozturkler, Christopher M Sandino +7
Deep learning (DL) has shown promise for faster, high quality accelerated MRI reconstruction. However, supervised DL methods depend on extensive amounts of fully-sampled (labeled)…
Quantitative Parametric Mapping of Tissues Properties from Standard Magnetic Resonance Imaging Enabled by Deep Learning
Yan Wu, Yajun Ma, Youngwook Kee +10
Magnetic resonance imaging (MRI) offers superior soft tissue contrast and is widely used in biomedicine. However, conventional MRI is not quantitative, which presents a bottleneck…
Recurrent Generative Adversarial Networks for Proximal Learning and Automated Compressive Image Recovery
Morteza Mardani, Hatef Monajemi, Vardan Papyan +3
Recovering images from undersampled linear measurements typically leads to an ill-posed linear inverse problem, that asks for proper statistical priors. Building effective priors i…
Deep Generative Adversarial Networks for Compressed Sensing Automates MRI
Morteza Mardani, Enhao Gong, Joseph Y. Cheng +8
Magnetic resonance image (MRI) reconstruction is a severely ill-posed linear inverse task demanding time and resource intensive computations that can substantially trade off {\it a…