mlsanityData to 7 Oct 2026

ReconResNet: Regularised Residual Learning for MR Image Reconstruction of Undersampled Cartesian and Radial Data

arXiv 2103.09203 ↗First posted 16 Mar 2021eess.IV cs.LGcited by 4

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Abstract (arXiv)

MRI is an inherently slow process, which leads to long scan time for high-resolution imaging. The speed of acquisition can be increased by ignoring parts of the data (undersampling). Consequently, this leads to the degradation of image quality, such as loss of resolution or introduction of image artefacts. This work aims to reconstruct highly undersampled Cartesian or radial MR acquisitions, with better resolution and with less to no artefact compared to conventional techniques like compressed sensing. In recent times, deep learning has emerged as a very important area of research and has shown immense potential in solving inverse problems, e.g. MR image reconstruction. In this paper, a deep learning based MR image reconstruction framework is proposed, which includes a modified regularised version of ResNet as the network backbone to remove artefacts from the undersampled image, followed by data consistency steps that fusions the network output with the data already available from undersampled k-space in order to further improve reconstruction quality. The performance of this framework for various undersampling patterns has also been tested, and it has been observed that the framework is robust to deal with various sampling patterns, even when mixed together while training, and results in very high quality reconstruction, in terms of high SSIM (highest being 0.990±0.006 for acceleration factor of 3.5), while being compared with the fully sampled reconstruction. It has been shown that the proposed framework can successfully reconstruct even for an acceleration factor of 20 for Cartesian (0.968±0.005) and 17 for radially (0.962±0.012) sampled data. Furthermore, it has been shown that the framework preserves brain pathology during reconstruction while being trained on healthy subjects.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 14 of its 58 references that are in the map. First posted on arXiv 16 Mar 2021.

Builds on

14 of 58 references.

References · 14

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2 more
  1. 2020Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Networkcited by 12
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Cited by year

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

4 papers.

  1. 2021Sinogram upsampling using Primal-Dual UNet for undersampled CT and radial MRI reconstructioncited by 6
  2. 2022Weakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classificationcited by 3
  3. 2023Complex Network for Complex Problems: A comparative study of CNN and Complex-valued CNNcited by 2
  4. 2022DDoS-UNet: Incorporating temporal information using Dynamic Dual-channel UNet for enhancing super-resolution of dynamic MRIcited by 1