100 citations · 518 across the 21 of their papers we have counts for
21 papers
Accelerated MRI With Deep Linear Convolutional Transform Learning
Hongyi Gu, Burhaneddin Yaman, Steen Moeller +2
Recent studies show that deep learning (DL) based MRI reconstruction outperforms conventional methods, such as parallel imaging and compressed sensing (CS), in multiple application…
Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging
Kerstin Hammernik, Thomas Küstner, Burhaneddin Yaman +4
Physics-driven deep learning methods have emerged as a powerful tool for computational magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new limits.…
On Instabilities of Conventional Multi-Coil MRI Reconstruction to Small Adverserial Perturbations
Chi Zhang, Jinghan Jia, Burhaneddin Yaman +4
Although deep learning (DL) has received much attention in accelerated MRI, recent studies suggest small perturbations may lead to instabilities in DL-based reconstructions, leadin…
Zero-Shot Self-Supervised Learning for MRI Reconstruction
Burhaneddin Yaman, Seyed Amir Hossein Hosseini, Mehmet Akçakaya
Deep learning (DL) has emerged as a powerful tool for accelerated MRI reconstruction, but often necessitates a database of fully-sampled measurements for training. Recent self-supe…
Self-Supervised Physics-Guided Deep Learning Reconstruction For High-Resolution 3D LGE CMR
Burhaneddin Yaman, Chetan Shenoy, Zilin Deng +4
Late gadolinium enhancement (LGE) cardiac MRI (CMR) is the clinical standard for diagnosis of myocardial scar. 3D isotropic LGE CMR provides improved coverage and resolution compar…
Improved Supervised Training of Physics-Guided Deep Learning Image Reconstruction with Multi-Masking
Burhaneddin Yaman, Seyed Amir Hossein Hosseini, Steen Moeller +1
Physics-guided deep learning (PG-DL) via algorithm unrolling has received significant interest for improved image reconstruction, including MRI applications. These methods unroll a…