Publication detail

Simultaneous Image Quality Improvement and Artefacts Correction in Accelerated MRI

Kanli, G. Perlo, D. Boudissa, S.Jirik, R. Keunen, O.

Original Title

Simultaneous Image Quality Improvement and Artefacts Correction in Accelerated MRI

Type

conference paper

Language

English

Original 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 acceleration and simultaneously artefacts correction without significant degradation, showcasing the model’s robustness in real-world settings.

Keywords

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

Authors

Kanli, G.; Perlo, D.; Boudissa, S.;Jirik, R.; Keunen, O.

Released

23. 10. 2024

Publisher

Springer, Cham

ISBN

9783031732836

Book

Lecture Notes in Computer Science

Edition

15241

ISBN

1611-3349

Periodical

Lecture Notes in Computer Science

State

Republic of Italy

Pages from

228

Pages to

237

Pages count

8

URL

https://link.springer.com/chapter/10.1007/978-3-031-73284-3_23

BibTex

@inproceedings{BUT190086,
  author="Kanli, G. and Perlo, D. and Boudissa, S. and Jirik, R. and Keunen, O.",
  title="Simultaneous Image Quality Improvement and Artefacts Correction in Accelerated MRI",
  booktitle="Lecture Notes in Computer Science",
  year="2024",
  series="15241",
  journal="Lecture Notes in Computer Science",
  pages="228--237",
  publisher="Springer, Cham",
  doi="10.1007/978-3-031-73284-3\{_}23",
  isbn="9783031732836",
  issn="1611-3349",
  url="https://link.springer.com/chapter/10.1007/978-3-031-73284-3_23"
}