mlsanityData to 7 Oct 2026

Towards Ultrafast MRI via Extreme k-Space Undersampling and Superresolution

arXiv 2103.02940 ↗First posted 4 Mar 2021cs.CV eess.IVcited by 5

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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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 17 of its 31 references that are in the map. First posted on arXiv 4 Mar 2021.

Builds on

17 of 31 references.

References · 17

  1. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  2. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,678
  3. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
  4. 2018fastMRI: An Open Dataset and Benchmarks for Accelerated MRIcited by 464
  5. 2017Image reconstruction by domain transform manifold learningcited by 231
  6. 2020A U-Net Based Discriminator for Generative Adversarial Networkscited by 128
  7. 2020Understanding SSIMcited by 75
  8. 2017Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentationcited by 66
  9. 2020Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challengecited by 59
  10. 2021Measuring Robustness in Deep Learning Based Compressive Sensingcited by 53
  11. 2020Experimental design for MRI by greedy policy searchcited by 40
  12. 2021B-spline Parameterized Joint Optimization of Reconstruction and K-space Trajectories (BJORK) for Accelerated 2D MRIcited by 29
5 more
  1. 2019MRI Super-Resolution with Ensemble Learning and Complementary Priorscited by 27
  2. 2020An Adaptive Intelligence Algorithm for Undersampled Knee MRI Reconstructioncited by 19
  3. 2020MRI Super-Resolution with GAN and 3D Multi-Level DenseNet: Smaller, Faster, and Bettercited by 15
  4. 2019Joint learning of cartesian undersampling and reconstruction for accelerated MRIcited by 14
  5. 2019Synthetic CT Generation from MRI Using Improved DualGANcited by 2

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Cited by year

Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 1.

5 papers.

  1. 2021Optimal MRI Undersampling Patterns for Ultimate Benefit of Medical Vision Taskscited by 7
  2. 2022Autofocusing+: Noise-Resilient Motion Correction in Magnetic Resonance Imagingcited by 5
  3. 2024Tumor likelihood estimation on MRI prostate data by utilizing k-Space informationcited by 3
  4. 2022Image Quality Assessment for Magnetic Resonance Imagingcited by 2
  5. 2023Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstructioncited by 2