1. Multicontrast MRI Super-Resolution via Transformer-Empowered Multiscale Contextual Matching and Aggregation
- Author
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Lyu, Jun, Li, Guangyuan, Wang, Chengyan, Cai, Qing, Dou, Qi, Zhang, David, and Qin, Jing
- Abstract
Magnetic resonance imaging (MRI) possesses the unique versatility to acquire images under a diverse array of distinct tissue contrasts, which makes multicontrast super-resolution (SR) techniques possible and needful. Compared with single-contrast MRI SR, multicontrast SR is expected to produce higher quality images by exploiting a variety of complementary information embedded in different imaging contrasts. However, existing approaches still have two shortcomings: 1) most of them are convolution-based methods and, hence, weak in capturing long-range dependencies, which are essential for MR images with complicated anatomical patterns and 2) they ignore to make full use of the multicontrast features at different scales and lack effective modules to match and aggregate these features for faithful SR. To address these issues, we develop a novel multicontrast MRI SR network via transformer-empowered multiscale feature matching and aggregation, dubbed McMRSR
$^{++}$ $^{++}$ - Published
- 2024
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