100 citations · 395 across the 34 of their papers we have counts for
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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.…
fastMRI+: Clinical Pathology Annotations for Knee and Brain Fully Sampled Multi-Coil MRI Data
Ruiyang Zhao, Burhaneddin Yaman, Yuxin Zhang +7
Improving speed and image quality of Magnetic Resonance Imaging (MRI) via novel reconstruction approaches remains one of the highest impact applications for deep learning in medica…
20-fold Accelerated 7T fMRI Using Referenceless Self-Supervised Deep Learning Reconstruction
Omer Burak Demirel, Burhaneddin Yaman, Logan Dowdle +7
High spatial and temporal resolution across the whole brain is essential to accurately resolve neural activities in fMRI. Therefore, accelerated imaging techniques target improved…
Improved Simultaneous Multi-Slice Functional MRI Using Self-supervised Deep Learning
Omer Burak Demirel, Burhaneddin Yaman, Logan Dowdle +7
Functional MRI (fMRI) is commonly used for interpreting neural activities across the brain. Numerous accelerated fMRI techniques aim to provide improved spatiotemporal resolutions.…
Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement
Mehmet Akçakaya, Burhaneddin Yaman, Hyungjin Chung +1
Recently, deep learning approaches have become the main research frontier for biological image reconstruction and enhancement problems thanks to their high performance, along with…
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…
Multi-Mask Self-Supervised Learning for Physics-Guided Neural Networks in Highly Accelerated MRI
Burhaneddin Yaman, Hongyi Gu, Seyed Amir Hossein Hosseini +5
Self-supervised learning has shown great promise due to its capability to train deep learning MRI reconstruction methods without fully-sampled data. Current self-supervised learnin…
Noise2Inpaint: Learning Referenceless Denoising by Inpainting Unrolling
Burhaneddin Yaman, Seyed Amir Hossein Hosseini, Mehmet Akçakaya
Deep learning based image denoising methods have been recently popular due to their improved performance. Traditionally, these methods are trained in a supervised manner, requiring…
High-Fidelity Accelerated MRI Reconstruction by Scan-Specific Fine-Tuning of Physics-Based Neural Networks
Seyed Amir Hossein Hosseini, Burhaneddin Yaman, Steen Moeller +1
Long scan duration remains a challenge for high-resolution MRI. Deep learning has emerged as a powerful means for accelerated MRI reconstruction by providing data-driven regularize…
Dense Recurrent Neural Networks for Accelerated MRI: History-Cognizant Unrolling of Optimization Algorithms
Seyed Amir Hossein Hosseini, Burhaneddin Yaman, Steen Moeller +2
Inverse problems for accelerated MRI typically incorporate domain-specific knowledge about the forward encoding operator in a regularized reconstruction framework. Recently physics…
Self-Supervised Physics-Based Deep Learning MRI Reconstruction Without Fully-Sampled Data
Burhaneddin Yaman, Seyed Amir Hossein Hosseini, Steen Moeller +3
Deep learning (DL) has emerged as a tool for improving accelerated MRI reconstruction. A common strategy among DL methods is the physics-based approach, where a regularized iterati…