HACR-Net: An Efficient hybrid attention network for MRI image super-resolution
Issued Date
2026-04-01
Resource Type
eISSN
19326203
Scopus ID
2-s2.0-105035244483
Journal Title
Plos One
Volume
21
Issue
4 April
Rights Holder(s)
SCOPUS
Bibliographic Citation
Plos One Vol.21 No.4 April (2026)
Suggested Citation
Muhammad A., Hajian A., Achakulvisut T., Aramvith S. HACR-Net: An Efficient hybrid attention network for MRI image super-resolution. Plos One Vol.21 No.4 April (2026). doi:10.1371/journal.pone.0345637 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/116202
Title
HACR-Net: An Efficient hybrid attention network for MRI image super-resolution
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Abstract
High-resolution Magnetic Resonance Imaging (MRI) plays an important role in clinical diagnosis and pathological assessment, due to its non-invasive nature and lack of ionizing radiation. However, the acquisition of high-resolution MRI is often constrained by hardware limitations and a prolonged scanning duration. To address these limitations, super-resolution (SR) techniques have been introduced to reconstruct high-resolution images from low-resolution inputs. However, despite these advances, existing methods often struggle to effectively extract shallow features, model complex contextual dependencies, and preserve fine anatomical details. To address these limitations, we propose a Hybrid Attention and Channel Retention Network (HACR-Net) for MRI image SR. HACR-Net incorporates a Hybrid Attention Module (HAM) to mitigate information loss during shallow feature extraction by jointly leveraging channel and spatial attention, enhancing informative features, and preserving spatially significant regions. A Multiscale Feature Aggregation Block (MFAB) is incorporated to capture global structural details, local texture, and high-frequency details. Complementing MFAB, the Channel Retention Attention Block (CRAB) enhances the recovery of fine contextual detail through a bottleneck design crafted to maintain a wider channel width and reduce information loss during feature compression. Extensive experiments on two benchmark datasets, IXI and BraTS2018, demonstrate that HACR-Net achieves high-performance reconstruction with only 1.67M parameters and 81.3G FLOPs, offering significant reductions in model size and computational cost compared to existing methods.