Dual contrast attention-guided multi-frequency fusion for multi-contrast MRI super-resolution.
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Abstract
Multi-contrast Magnetic Resonance (MR) imaging super-resolution (SR) reconstruction
is an effective solution for acquiring high-resolution MR images. It utilizes
anatomical information from auxiliary contrast images to improve the quality of the target
contrast images. However, existing studies have simply explored the relationships between
auxiliary contrast and target contrast images but did not fully consider different anatomical
information contained in multi-contrast images, resulting in texture details and artifacts
unrelated to the target contrast images.To address these issues, we propose a
dual contrast attention-guided multi-frequency fusion (DCAMF) network to reconstruct SR
MR images from low-resolution MR images, which adaptively captures relevant anatomical
information and processes the texture details and low-frequency information from multicontrast
images in parallel. Specifically, after the feature extraction, a feature selection
module based on a dual contrast attention mechanism is proposed to focus on the texture
details of the auxiliary contrast images and the low-frequency features of the target contrast
images. Then, based on the characteristics of the selected features, a high- and low-frequency
fusion decoder is constructed to fuse these features. In addition, a texture-enhancing module
is embedded in the high-frequency fusion decoder, to highlight and refine the texture details
of the auxiliary contrast and target contrast images. Finally, the high- and low-frequency
fusion process is constrained by integrating a deeply-supervised mechanism into the DCAMF
network.The experimental results show that the DCAMF outperforms
other state-of-the-art methods. The PSNR and SSIM of DCAMF are 39.02 dB and
0.9771 on the IXI dataset and 37.59 dB and 0.9770 on the BraTS2018 dataset,
respectively. The image recovery is further validated in segmentation tasks.
Our proposed SR model can enhance the quality of MR images. The results of the SR
study provide a reliable basis for clinical diagnosis and subsequent image-guided treatment.© 2023 Institute of Physics and Engineering in Medicine.