ArticleIEEE journal of selected topics in signal processing2020
Dense Recurrent Neural Networks for Accelerated MRI: History-Cognizant Unrolling of Optimization Algorithms.
Article in IEEE journal of selected topics in signal processing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review and Identification of the Challenges of Deep Learning Techniques for Undersampled Magnetic Resonance Image Reconstruction.Sensors (Basel, Switzerland) · 2024Pooled it
- CONSISTENCY MODELS FOR FAST MRI USING REGULARIZATION BY DENOISING.Proceedings. IEEE International Symposium on Biomedical Imaging · 2026Article
- Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining.ArXiv · 2025Article
- Application of Mask R-CNN for automatic recognition of teeth and caries in cone-beam computerized tomography.BMC oral health · 2025Article
- Deep Learning Assisted Outer Volume Removal for Highly-Accelerated Real-Time Dynamic MRI.ArXiv · 2025Article
- Article
- CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mapping.Journal of mathematical imaging and vision · 2025Article
- Time-Embedded Algorithm Unrolling for Computational MRI.Advances in neural information processing systems · 2025Article
- Interpretable deep learning for deconvolutional analysis of neural signals.bioRxiv : the preprint server for biology · 2024Article
- A review of deep learning-based reconstruction methods for accelerated MRI using spatiotemporal and multi-contrast redundancies.Biomedical engineering letters · 2024Review
- Accelerated cardiac cine magnetic resonance imaging using deep low-rank plus sparse network: validation in patients.Quantitative imaging in medicine and surgery · 2024Article
- Physics-Driven Deep Learning Reconstruction of Frequency-Modulated Rabi-Encoded Echoes for Faster Accessible MRI.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024Article
- Accelerated MRI reconstructions via variational network and feature domain learning.Scientific reports · 2024Article
- NON-CARTESIAN SELF-SUPERVISED PHYSICS-DRIVEN DEEP LEARNING RECONSTRUCTION FOR HIGHLY-ACCELERATED MULTI-ECHO SPIRAL FMRI.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- CYCLE-CONSISTENT SELF-SUPERVISED LEARNING FOR IMPROVED HIGHLY-ACCELERATED MRI RECONSTRUCTION.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- A CONVEX COMPRESSIBILITY-INSPIRED UNSUPERVISED LOSS FUNCTION FOR PHYSICS-DRIVEN DEEP LEARNING RECONSTRUCTION.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- Learning Task-Specific Strategies for Accelerated MRI.IEEE transactions on computational imaging · 2024Article
- Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- A Hybrid Residual Attention Convolutional Neural Network for Compressed Sensing Magnetic Resonance Image Reconstruction.Diagnostics (Basel, Switzerland) · 2023Article
- A densely interconnected network for deep learning accelerated MRI.Magma (New York, N.Y.) · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
Inverse problems for accelerated MRI typically incorporate domain-specific knowledge about the forward encoding operator in a regularized reconstruction framework. Recently physics-driven deep learning (DL) methods have been proposed to use neural networks for data-driven regularization. These methods unroll iterative optimization algorithms to solve the inverse problem objective function, by alternating between domain-specific data consistency and data-driven regularization via neural networks. The whole unrolled network is then trained end-to-end to learn the parameters of the network. Due to simplicity of data consistency updates with gradient descent steps, proximal gradient descent (PGD) is a common approach to unroll physics-driven DL reconstruction methods. However, PGD methods have slow convergence rates, necessitating a higher number of unrolled iterations, leading to memory issues in training and slower reconstruction times in testing. Inspired by efficient variants of PGD methods that use a history of the previous iterates, we propose a history-cognizant unrolling of the optimization algorithm with dense connections across iterations for improved performance. In our approach, the gradient descent steps are calculated at a trainable combination of the outputs of all the previous regularization units. We also apply this idea to unrolling variable splitting methods with quadratic relaxation. Our results in reconstruction of the fastMRI knee dataset show that the proposed history-cognizant approach reduces residual aliasing artifacts compared to its conventional unrolled counterpart without requiring extra computational power or increasing reconstruction time.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.