Simultaneous Image Quality Improvement and Artefacts Correction in Accelerated MRI

Georgia Kanli*, Daniele Perlo, Selma Boudissa, Radovan Jiřík, Olivier Keunen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

MR data are acquired in the frequency domain, known as k-space. Acquiring high-quality and high-resolution MR images can be time-consuming, posing a significant challenge when multiple sequences providing complementary contrast information are needed or when the patient is unable to remain in the scanner for an extended period of time. Reducing k-space measurements is a strategy to speed up acquisition, but often leads to reduced quality in reconstructed images. Additionally, in real-world MRI, both under-sampled and full-sampled images are prone to artefacts, and correcting these artefacts is crucial for maintaining diagnostic accuracy. Deep learning methods have been proposed to restore image quality from under-sampled data, while others focused on the correction of artefacts that result from the noise or motion. No approach has however been proposed so far that addresses both acceleration and artefacts correction, limiting the performance of these models when these degradation factors occur simultaneously. To address this gap, we present a method for recovering high-quality images from under-sampled data with simultaneously correction for noise and motion artefact called USArt (Under-Sampling and Artifact correction model). Customized for 2D brain anatomical images acquired with Cartesian sampling, USArt employs a dual sub-model approach. The results demonstrate remarkable increase of signal-to-noise ratio (SNR) and contrast in the images restored. Various under-sampling strategies and degradation levels were explored, with the gradient under-sampling strategy yielding the best outcomes. We achieved up to 5× acceleration and simultaneously artefacts correction without significant degradation, showcasing the model’s robustness in real-world settings.

Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging - 15th International Workshop, MLMI 2024, Held in Conjunction with MICCAI 2024, Proceedings
EditorsXuanang Xu, Zhiming Cui, Kaicong Sun, Islem Rekik, Xi Ouyang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages228-237
Number of pages10
ISBN (Print)9783031732836
DOIs
Publication statusPublished - 23 Oct 2024
Event15th International Workshop on Machine Learning in Medical Imaging, MLMI 2024 was held in conjunction with the 27th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024 - Marrakesh, Morocco
Duration: 6 Oct 20246 Oct 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume15241 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th International Workshop on Machine Learning in Medical Imaging, MLMI 2024 was held in conjunction with the 27th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024
Country/TerritoryMorocco
CityMarrakesh
Period6/10/246/10/24

Keywords

  • acceleration
  • artefact/noise correction
  • deep learning
  • magnetic resonance imaging
  • under-sampling

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