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• W4380883172 abstract "High-quality magnetic resonance imaging (MRI) affords clear body tissue structure for reliable diagnosing. However, there is a principal problem of the trade-off between acquisition speed and image quality. Image reconstruction and super-resolution are crucial techniques to solve these problems. In the main field of MR image restoration, most researchers mainly focus on only one of these aspects, namely reconstruction or super-resolution. In this paper, we propose an efficient model called Multi-Stage Hybrid Attention Network (MHAN) that performs the multi-task of recovering high-resolution (HR) MR images from low-resolution (LR) under-sampled measurements. Our model is highlighted by three major modules: (i) an Amplified Spatial Attention Block (ASAB) capable of enhancing the differences in spatial information, (ii) a Self-Attention Block with a Data-Consistency Layer (DC-SAB), which can improve the accuracy of the extracted feature information, (iii) an Adaptive Local Residual Attention Block (ALRAB) that focuses on both spatial and channel information. MHAN employs an encoder-decoder architecture to deeply extract contextual information and a pipeline to provide spatial accuracy. Compared with the recent multi-task model T2Net, our MHAN improves by 2.759 dB in PSNR and 0.026 in SSIM with scaling factor ×2 and acceleration factor 4× on T2 modality." @default.
• W4380883172 created "2023-06-17" @default.
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• W4380883172 date "2023-09-01" @default.
• W4380883172 modified "2023-09-27" @default.
• W4380883172 title "MHAN: Multi-Stage Hybrid Attention Network for MRI reconstruction and super-resolution" @default.
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• W4380883172 doi "https://doi.org/10.1016/j.compbiomed.2023.107181" @default.
• W4380883172 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37352637" @default.
• W4380883172 hasPublicationYear "2023" @default.
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