ArticleIEEE transactions on medical imaging2024
ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformer.
Article in IEEE transactions on medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Multi-Frequency Feature Guided Progressive Divide-and-Conquer Network for Accelerated MRI Reconstruction.Journal of imaging informatics in medicine · 2026Article
- Benchmarking Hybrid CNN-Transformer Versus Pure Transformer Architectures for Accelerated HyperpolarizedJournal of magnetic resonance imaging : JMRI · 2026Article
- Image Distortion Correction for Diffusion MR Imaging Using a Transformer-based U-Net.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026Article
- Dynamic Transformer Based on Wavelet and Diffusion Prior Guidance for Cardiac Cine MRI Reconstruction.Sensors (Basel, Switzerland) · 2026Article
- Radon-Guided Wavelet-Domain Attention U-Net for Periodic Artifact Suppression in Brain MRI.Journal of imaging · 2026Article
- Self-supervised learning for MRI reconstruction: a review and new perspective.Magma (New York, N.Y.) · 2025Review
- Magnetic resonance image processing transformer for general accelerated image restoration.Scientific reports · 2025Article
- Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing Integration.ArXiv · 2025Article
- Regularization by neural style transfer for MRI field-transfer reconstruction with limited data.Frontiers in artificial intelligence · 2025Article
- Transformers and large language models in healthcare: A review.Artificial intelligence in medicine · 2024Review
- Deep learning for accelerated and robust MRI reconstruction.Magma (New York, N.Y.) · 2024Review
- Fast MRI Reconstruction Using Deep Learning-based Compressed Sensing: A Systematic Review.ArXiv · 2024Article
- Enhancing the Super-Resolution of Medical Images: Introducing the Deep Residual Feature Distillation Channel Attention Network for Optimized Performance and Efficiency.Bioengineering (Basel, Switzerland) · 2023Article
- A transformer-based hierarchical registration framework for multimodality deformable image registration.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2023Article
- Vision transformer architecture and applications in digital health: a tutorial and survey.Visual computing for industry, biomedicine, and art · 2023Review
- Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives.Medical image analysis · 2023Review
- Survey on natural language processing in medical image analysis.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2022Article
- Deep learning for fast low-field MRI acquisitions.Scientific reports · 2022Article
- Swin transformer-based GAN for multi-modal medical image translation.Frontiers in oncology · 2022Article
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5 authors.
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Abstract
The accelerating magnetic resonance imaging (MRI) reconstruction process is a challenging ill-posed inverse problem due to the excessive under-sampling operation in k -space. In this paper, we propose a recurrent Transformer model, namely ReconFormer, for MRI reconstruction, which can iteratively reconstruct high-fidelity magnetic resonance images from highly under-sampled k -space data (e.g., up to 8× acceleration). In particular, the proposed architecture is built upon Recurrent Pyramid Transformer Layers (RPTLs). The core design of the proposed method is Recurrent Scale-wise Attention (RSA), which jointly exploits intrinsic multi-scale information at every architecture unit as well as the dependencies of the deep feature correlation through recurrent states. Moreover, benefiting from its recurrent nature, ReconFormer is lightweight compared to other baselines and only contains 1.1 M trainable parameters. We validate the effectiveness of ReconFormer on multiple datasets with different magnetic resonance sequences and show that it achieves significant improvements over the state-of-the-art methods with better parameter efficiency. The implementation code and pre-trained weights are available at https://github.com/guopengf/ReconFormer.
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