# An Adaptive Intelligence Algorithm for Undersampled Knee MRI Reconstruction

arXiv: https://arxiv.org/abs/2004.07339 · HTML: https://mlsanity.com/paper/2004.07339/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2020.

## Built on by (most cited first, 19 of 19)

- [Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging](https://mlsanity.com/paper/2203.12215/) (2022)
- [An Optimal Control Framework for Joint-channel Parallel MRI Reconstruction without Coil Sensitivities](https://mlsanity.com/paper/2109.09738/) (2021)
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- [Towards Ultrafast MRI via Extreme k-Space Undersampling and Superresolution](https://mlsanity.com/paper/2103.02940/) (2021)
- [Learned Half-Quadratic Splitting Network for MR Image Reconstruction](https://mlsanity.com/paper/2112.09760/) (2021)
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- [Density Compensated Unrolled Networks for Non-Cartesian MRI Reconstruction](https://mlsanity.com/paper/2101.01570/) (2021)
- [Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstruction](https://mlsanity.com/paper/2111.15498/) (2021)
- [A Plug-and-Play Method for Guided Multi-contrast MRI Reconstruction based on Content/Style Modeling](https://mlsanity.com/paper/2409.13477/) (2024)
- [ASL to PET Translation by a Semi-supervised Residual-based Attention-guided Convolutional Neural Network](https://mlsanity.com/paper/2103.05116/) (2021)
- [Noise2Recon: Enabling Joint MRI Reconstruction and Denoising with Semi-Supervised and Self-Supervised Learning](https://mlsanity.com/paper/2110.00075/) (2021)
- [Deep learning-based reconstruction of highly accelerated 3D MRI](https://mlsanity.com/paper/2203.04674/) (2022)
- [Image Quality Assessment for Magnetic Resonance Imaging](https://mlsanity.com/paper/2203.07809/) (2022)
- [A Densely Interconnected Network for Deep Learning Accelerated MRI](https://mlsanity.com/paper/2207.02073/) (2022)
- [Efficient Unrolled Networks for Large-Scale 3D Inverse Problems](https://mlsanity.com/paper/2601.02141/) (2026)
- [MimicBot: Combining Imitation and Reinforcement Learning to win in Bot Bowl](https://mlsanity.com/paper/2108.09478/) (2021)
- [SynthRAR: Ring Artifacts Reduction in CT with Unrolled Network and Synthetic Data Training](https://mlsanity.com/paper/2602.11880/) (2026)
- [Enhancing Implicit Neural Representations with Image Feature Embedding for Unsupervised Cardiac Cine MRI Reconstruction](https://mlsanity.com/paper/2607.04069/) (2026)
- [Data-Efficient Networks for Multi-Contrast MRI Reconstruction based on a Generalized Content/Style Prior](https://mlsanity.com/paper/2609.01959/) (2026)

## Abstract (arXiv)

Adaptive intelligence aims at empowering machine learning techniques with the additional use of domain knowledge. In this work, we present the application of adaptive intelligence to accelerate MR acquisition. Starting from undersampled k-space data, an iterative learning-based reconstruction scheme inspired by compressed sensing theory is used to reconstruct the images. We adopt deep neural networks to refine and correct prior reconstruction assumptions given the training data. The network was trained and tested on a knee MRI dataset from the 2019 fastMRI challenge organized by Facebook AI Research and NYU Langone Health. All submissions to the challenge were initially ranked based on similarity with a known groundtruth, after which the top 4 submissions were evaluated radiologically. Our method was evaluated by the fastMRI organizers on an independent challenge dataset. It ranked #1, shared #1, and #3 on respectively the 8x accelerated multi-coil, the 4x multi-coil, and the 4x single-coil track. This demonstrates the superior performance and wide applicability of the method.