TY - GEN
T1 - Simultaneous Image Quality Improvement and Artefacts Correction in Accelerated MRI
AU - Kanli, Georgia
AU - Perlo, Daniele
AU - Boudissa, Selma
AU - Jiřík, Radovan
AU - Keunen, Olivier
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2024/10/23
Y1 - 2024/10/23
N2 - 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.
AB - 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.
KW - acceleration
KW - artefact/noise correction
KW - deep learning
KW - magnetic resonance imaging
KW - under-sampling
UR - http://www.scopus.com/inward/record.url?scp=85208254835&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-73284-3_23
DO - 10.1007/978-3-031-73284-3_23
M3 - Conference contribution
AN - SCOPUS:85208254835
SN - 9783031732836
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 228
EP - 237
BT - Machine Learning in Medical Imaging - 15th International Workshop, MLMI 2024, Held in Conjunction with MICCAI 2024, Proceedings
A2 - Xu, Xuanang
A2 - Cui, Zhiming
A2 - Sun, Kaicong
A2 - Rekik, Islem
A2 - Ouyang, Xi
PB - Springer Science and Business Media Deutschland GmbH
T2 - 15th 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
Y2 - 6 October 2024 through 6 October 2024
ER -