# Towards Ultrafast MRI via Extreme k-Space Undersampling and Superresolution

arXiv: https://arxiv.org/abs/2103.02940 · HTML: https://mlsanity.com/paper/2103.02940/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 31 references that are in the map.
- First posted on arXiv 4 Mar 2021.

## Builds on (most cited first, 17 of 17)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [fastMRI: An Open Dataset and Benchmarks for Accelerated MRI](https://mlsanity.com/paper/1811.08839/) (2018)
- [Image reconstruction by domain transform manifold learning](https://mlsanity.com/paper/1704.08841/) (2017)
- [A U-Net Based Discriminator for Generative Adversarial Networks](https://mlsanity.com/paper/2002.12655/) (2020)
- [Understanding SSIM](https://mlsanity.com/paper/2006.13846/) (2020)
- [Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation](https://mlsanity.com/paper/1702.07841/) (2017)
- [Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge](https://mlsanity.com/paper/2001.02518/) (2020)
- [Measuring Robustness in Deep Learning Based Compressive Sensing](https://mlsanity.com/paper/2102.06103/) (2021)
- [Experimental design for MRI by greedy policy search](https://mlsanity.com/paper/2010.16262/) (2020)
- [B-spline Parameterized Joint Optimization of Reconstruction and K-space Trajectories (BJORK) for Accelerated 2D MRI](https://mlsanity.com/paper/2101.11369/) (2021)
- [MRI Super-Resolution with Ensemble Learning and Complementary Priors](https://mlsanity.com/paper/1907.03063/) (2019)
- [An Adaptive Intelligence Algorithm for Undersampled Knee MRI Reconstruction](https://mlsanity.com/paper/2004.07339/) (2020)
- [MRI Super-Resolution with GAN and 3D Multi-Level DenseNet: Smaller, Faster, and Better](https://mlsanity.com/paper/2003.01217/) (2020)
- [Joint learning of cartesian undersampling and reconstruction for accelerated MRI](https://mlsanity.com/paper/1905.09324/) (2019)
- [Synthetic CT Generation from MRI Using Improved DualGAN](https://mlsanity.com/paper/1909.08942/) (2019)

## Built on by (most cited first, 5 of 5)

- [Optimal MRI Undersampling Patterns for Ultimate Benefit of Medical Vision Tasks](https://mlsanity.com/paper/2108.04914/) (2021)
- [Autofocusing+: Noise-Resilient Motion Correction in Magnetic Resonance Imaging](https://mlsanity.com/paper/2203.05569/) (2022)
- [Tumor likelihood estimation on MRI prostate data by utilizing k-Space information](https://mlsanity.com/paper/2407.06165/) (2024)
- [Image Quality Assessment for Magnetic Resonance Imaging](https://mlsanity.com/paper/2203.07809/) (2022)
- [Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction](https://mlsanity.com/paper/2305.16376/) (2023)

## Abstract (arXiv)

We went below the MRI acceleration factors (a.k.a., k-space undersampling) reported by all published papers that reference the original fastMRI challenge, and then considered powerful deep learning based image enhancement methods to compensate for the underresolved images. We thoroughly study the influence of the sampling patterns, the undersampling and the downscaling factors, as well as the recovery models on the final image quality for both the brain and the knee fastMRI benchmarks. The quality of the reconstructed images surpasses that of the other methods, yielding an MSE of 0.00114, a PSNR of 29.6 dB, and an SSIM of 0.956 at x16 acceleration factor. More extreme undersampling factors of x32 and x64 are also investigated, holding promise for certain clinical applications such as computer-assisted surgery or radiation planning. We survey 5 expert radiologists to assess 100 pairs of images and show that the recovered undersampled images statistically preserve their diagnostic value.