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MAMOC: MRI Motion Correction via Masked Autoencoding

arXiv 2405.14590 ↗First posted 23 May 2024eess.IV cs.CVcited by 0

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Abstract (arXiv)

The presence of motion artifacts in magnetic resonance imaging (MRI) scans poses a significant challenge, where even minor patient movements can lead to artifacts that may compromise the scan's utility.This paper introduces MAsked MOtion Correction (MAMOC), a novel method designed to address the issue of Retrospective Artifact Correction (RAC) in motion-affected MRI brain scans. MAMOC uses masked autoencoding self-supervision, transfer learning and test-time prediction to efficiently remove motion artifacts, producing high-fidelity, native-resolution scans. Until recently, realistic, openly available paired artifact presentations for training and evaluating retrospective motion correction methods did not exist, making it necessary to simulate motion artifacts. Leveraging the MR-ART dataset and bigger unlabeled datasets (ADNI, OASIS-3, IXI), this work is the first to evaluate motion correction in MRI scans using real motion data on a public dataset, showing that MAMOC achieves improved performance over existing motion correction methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 37 references that are in the map. First posted on arXiv 23 May 2024.

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15 of 37 references.

References · 15

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
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  5. 2021SwinIR: Image Restoration Using Swin Transformercited by 1,887
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  7. 2017Mixed Precision Trainingcited by 1,156
  8. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 830
  9. 2018Noise2Noise: Learning Image Restoration without Clean Datacited by 533
  10. 2019FastSurfer -- A fast and accurate deep learning based neuroimaging pipelinecited by 112
  11. 2018Evaluating the Impact of Intensity Normalization on MR Image Synthesiscited by 26
  12. 2023Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Reviewcited by 18
3 more
  1. 2020Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Networkcited by 12
  2. 2022A Novel Sampling Scheme for Text- and Image-Conditional Image Synthesis in Quantized Latent Spacescited by 11
  3. 2022Wide Range MRI Artifact Removal with Transformerscited by 2

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