# XPDNet for MRI Reconstruction: an application to the 2020 fastMRI challenge

arXiv: https://arxiv.org/abs/2010.07290 · HTML: https://mlsanity.com/paper/2010.07290/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2020.

## Built on by (most cited first, 13 of 13)

- [Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging](https://mlsanity.com/paper/2203.12215/) (2022)
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- [HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstruction](https://mlsanity.com/paper/2203.08213/) (2022)
- [A study of why we need to reassess full reference image quality assessment with medical images](https://mlsanity.com/paper/2405.19097/) (2024)
- [A study on the adequacy of common IQA measures for medical images](https://mlsanity.com/paper/2405.19224/) (2024)
- [Dual-Domain Reconstruction Networks with V-Net and K-Net for fast MRI](https://mlsanity.com/paper/2203.05725/) (2022)
- [CAMP-Net: Consistency-Aware Multi-Prior Network for Accelerated MRI Reconstruction](https://mlsanity.com/paper/2306.11238/) (2023)
- [Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction using the Local Lipschitz](https://mlsanity.com/paper/2305.07618/) (2023)
- [Meta-Prior: Meta learning for Adaptive Inverse Problem Solvers](https://mlsanity.com/paper/2311.18710/) (2023)
- [Training-Free Adversarial Robustness in Computational MRI](https://mlsanity.com/paper/2501.01908/) (2025)
- [CARE: Training-Free Controllable Restoration for Medical Images via Dual-Latent Steering](https://mlsanity.com/paper/2603.25026/) (2026)
- [OpenMASC: An Open-Source Pipeline for Cross-Trajectory Metal-Aware Sampling and Correction in Accelerated MRI](https://mlsanity.com/paper/2609.32343/) (2026)

## 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.