Self-supervised deep equilibrium models with theoretical guarantees and applications to MRI reconstruction

W Gan, C Ying, PE Boroojeni, T Wang… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Deep equilibrium models (DEQ) have emerged as a powerful alternative to deep unfolding
(DU) for image reconstruction. DEQ models—implicit neural networks with effectively infinite …

Self-supervised dynamic MRI reconstruction

M Acar, T Çukur, İ Öksüz - … Learning for Medical Image Reconstruction: 4th …, 2021 - Springer
Deep learning techniques have recently been adopted for accelerating dynamic MRI
acquisitions. Yet, common frameworks for model training rely on availability of large sets of …

Progressive Divide-and-Conquer via Subsampling Decomposition for Accelerated MRI

C Wang, L Guo, Y Wang, H Cheng… - Proceedings of the …, 2024 - openaccess.thecvf.com
Deep unfolding networks (DUN) have emerged as a popular iterative framework for
accelerated magnetic resonance imaging (MRI) reconstruction. However conventional DUN …

Deep MRI reconstruction with generative vision transformers

Y Korkmaz, M Yurt, SUH Dar, M Özbey… - Machine Learning for …, 2021 - Springer
Supervised training of deep network models for MRI reconstruction requires access to large
databases of fully-sampled MRI acquisitions. To alleviate dependency on costly databases …

Stable deep MRI reconstruction using generative priors

M Zach, F Knoll, T Pock - IEEE Transactions on Medical …, 2023 - ieeexplore.ieee.org
Data-driven approaches recently achieved remarkable success in magnetic resonance
imaging (MRI) reconstruction, but integration into clinical routine remains challenging due to …

Self-supervised MRI reconstruction with unrolled diffusion models

Y Korkmaz, T Cukur, VM Patel - International Conference on Medical …, 2023 - Springer
Abstract Magnetic Resonance Imaging (MRI) produces excellent soft tissue contrast, albeit it
is an inherently slow imaging modality. Promising deep learning methods have recently …

Self-Supervised Deep Unrolled Reconstruction Using Regularization by Denoising

P Huang, C Zhang, X Zhang, X Li… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Deep learning methods have been successfully used in various computer vision tasks.
Inspired by that success, deep learning has been explored in magnetic resonance imaging …

DC-SiamNet: Deep contrastive Siamese network for self-supervised MRI reconstruction

Y Yan, T Yang, X Zhao, C Jiao, A Yang… - Computers in Biology and …, 2023 - Elsevier
Reconstruction methods based on deep learning have greatly shortened the data
acquisition time of magnetic resonance imaging (MRI). However, these methods typically …

Deep unfolding architecture for MRI reconstruction enhanced by adaptive noise maps

A Aghabiglou, EM Eksioglu - Biomedical Signal Processing and Control, 2022 - Elsevier
Unfolding provides a potent method to improve deep network performance in image
restoration problems. Recent results in the literature have demonstrated the improvement …

NPB-REC: A non-parametric Bayesian deep-learning approach for undersampled MRI reconstruction with uncertainty estimation

S Khawaled, M Freiman - Artificial Intelligence in Medicine, 2024 - Elsevier
The ability to reconstruct high-quality images from undersampled MRI data is vital in
improving MRI temporal resolution and reducing acquisition times. Deep learning methods …