XPDNet for MRI Reconstruction: an application to the 2020 fastMRI challenge
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Abstract (arXiv)
We present a new neural network, the XPDNet, for MRI reconstruction from periodically under-sampled multi-coil data. We inform the design of this network by taking best practices from MRI reconstruction and computer vision. We show that this network can achieve state-of-the-art reconstruction results, as shown by its ranking of second in the fastMRI 2020 challenge.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Oct 2020.
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Citing papers per year: 2021: 1; 2022: 4; 2023: 3; 2024: 2; 2025: 1; 2026: 2.
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- 2022Physics-Driven Deep Learning for Computational Magnetic Resonance Imagingcited by 49
- 2022Bayesian MRI Reconstruction with Joint Uncertainty Estimation using Diffusion Modelscited by 46
- 2021Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI Reconstructioncited by 34
- 2022HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstructioncited by 30
- 2024A study of why we need to reassess full reference image quality assessment with medical imagescited by 16
- 2024A study on the adequacy of common IQA measures for medical imagescited by 10
- 2022Dual-Domain Reconstruction Networks with V-Net and K-Net for fast MRIcited by 8
- 2023CAMP-Net: Consistency-Aware Multi-Prior Network for Accelerated MRI Reconstructioncited by 3
- 2023Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction using the Local Lipschitzcited by 2
- 2023Meta-Prior: Meta learning for Adaptive Inverse Problem Solverscited by 0
- 2025Training-Free Adversarial Robustness in Computational MRIcited by 0
- 2026CARE: Training-Free Controllable Restoration for Medical Images via Dual-Latent Steeringcited by 0
- 2026OpenMASC: An Open-Source Pipeline for Cross-Trajectory Metal-Aware Sampling and Correction in Accelerated MRI26 Sep 2026
- 2026CARE: Training-Free Controllable Restoration for Medical Images via Dual-Latent Steering26 Mar 2026
- 2025Training-Free Adversarial Robustness in Computational MRI3 Jan 2025
- 2024A study on the adequacy of common IQA measures for medical images29 May 2024
- 2024A study of why we need to reassess full reference image quality assessment with medical images29 May 2024
- 2023Meta-Prior: Meta learning for Adaptive Inverse Problem Solvers30 Nov 2023
- 2023CAMP-Net: Consistency-Aware Multi-Prior Network for Accelerated MRI Reconstruction20 Jun 2023
- 2023Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction using the Local Lipschitz12 May 2023
- 2022Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging23 Mar 2022
- 2022HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstruction15 Mar 2022
- 2022Dual-Domain Reconstruction Networks with V-Net and K-Net for fast MRI11 Mar 2022
- 2022Bayesian MRI Reconstruction with Joint Uncertainty Estimation using Diffusion Models3 Feb 2022
- 2022Physics-Driven Deep Learning for Computational Magnetic Resonance Imagingcited by 49
- 2021Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI Reconstructioncited by 34
- 2022HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstructioncited by 30
- 2024A study of why we need to reassess full reference image quality assessment with medical imagescited by 16
- 2024A study on the adequacy of common IQA measures for medical imagescited by 10
- 2022Dual-Domain Reconstruction Networks with V-Net and K-Net for fast MRIcited by 8
- 2023CAMP-Net: Consistency-Aware Multi-Prior Network for Accelerated MRI Reconstructioncited by 3
- 2023Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction using the Local Lipschitzcited by 2
- 2023Meta-Prior: Meta learning for Adaptive Inverse Problem Solverscited by 0
- 2025Training-Free Adversarial Robustness in Computational MRIcited by 0
- 2026CARE: Training-Free Controllable Restoration for Medical Images via Dual-Latent Steeringcited by 0
- 2026OpenMASC: An Open-Source Pipeline for Cross-Trajectory Metal-Aware Sampling and Correction in Accelerated MRIcited by 0
- 2026OpenMASC: An Open-Source Pipeline for Cross-Trajectory Metal-Aware Sampling and Correction in Accelerated MRI26 Sep 2026
- 2026CARE: Training-Free Controllable Restoration for Medical Images via Dual-Latent Steering26 Mar 2026
- 2025Training-Free Adversarial Robustness in Computational MRI3 Jan 2025
- 2024A study on the adequacy of common IQA measures for medical images29 May 2024
- 2024A study of why we need to reassess full reference image quality assessment with medical images29 May 2024
- 2023Meta-Prior: Meta learning for Adaptive Inverse Problem Solvers30 Nov 2023
- 2023CAMP-Net: Consistency-Aware Multi-Prior Network for Accelerated MRI Reconstruction20 Jun 2023
- 2023Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction using the Local Lipschitz12 May 2023
- 2022Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging23 Mar 2022
- 2022HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstruction15 Mar 2022
- 2022Dual-Domain Reconstruction Networks with V-Net and K-Net for fast MRI11 Mar 2022
- 2021Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI Reconstruction18 Nov 2021