Flexible Alignment Super-Resolution Network for Multi-Contrast MRI

• URL: http://arxiv.org/abs/2210.03460v1
• Date: Fri, 7 Oct 2022 11:07:20 GMT
• Title: Flexible Alignment Super-Resolution Network for Multi-Contrast MRI
• Authors: Yiming Liu, Mengxi Zhang, Weiqin Zhang, Bo Hou, Dan Liu, Heqing Lian, Bo Jiang
• Abstract summary: Super-Resolution plays a crucial role in preprocessing the low-resolution images for more precise medical analysis. We propose the Flexible Alignment Super-Resolution Network (FASR-Net) for multi-contrast magnetic resonance images Super-Resolution.
• Score: 7.727046305845654
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Magnetic resonance images play an essential role in clinical diagnosis by acquiring the structural information of biological tissue. However, during acquiring magnetic resonance images, patients have to endure physical and psychological discomfort, including irritating noise and acute anxiety. To make the patient feel cozier, technically, it will reduce the retention time that patients stay in the strong magnetic field at the expense of image quality. Therefore, Super-Resolution plays a crucial role in preprocessing the low-resolution images for more precise medical analysis. In this paper, we propose the Flexible Alignment Super-Resolution Network (FASR-Net) for multi-contrast magnetic resonance images Super-Resolution. The core of multi-contrast SR is to match the patches of low-resolution and reference images. However, the inappropriate foreground scale and patch size of multi-contrast MRI sometimes lead to the mismatch of patches. To tackle this problem, the Flexible Alignment module is proposed to endow receptive fields with flexibility. Flexible Alignment module contains two parts: (1) The Single-Multi Pyramid Alignmet module serves for low-resolution and reference image with different scale. (2) The Multi-Multi Pyramid Alignment module serves for low-resolution and reference image with the same scale. Extensive experiments on the IXI and FastMRI datasets demonstrate that the FASR-Net outperforms the existing state-of-the-art approaches. In addition, by comparing the reconstructed images with the counterparts obtained by the existing algorithms, our method could retain more textural details by leveraging multi-contrast images.

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