100 citations · 296 across the 8 of their papers we have counts for
8 papers
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…
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…
Accelerated Coronary MRI with sRAKI: A Database-Free Self-Consistent Neural Network k-space Reconstruction for Arbitrary Undersampling
Seyed Amir Hossein Hosseini, Chi Zhang, Sebastian Weingärtner +4
This study aims to accelerate coronary MRI using a novel reconstruction algorithm, called self-consistent robust artificial-neural-networks for k-space interpolation (sRAKI). sRAKI…