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85 / 2021-10-24 23:34:28
SMIR: A Transformer-Based Model for MRI super-resolution reconstruction
Transformers,Magnetic resonance imaging (MRI),Deep learning,Super-resolution Reconstruction
Magnetic Resonance Imaging（MRI/fMRI）
终稿
超 严 / 北京理工大学
根 石 / 北京理工大学计算机学院
正良 吴 / 北京理工大学
Down-sampling magnetic resonance imaging super-resolution reconstruction is one of the main problems in the field of accelerating magnetic resonance imaging research. The current method with better results is the traditional method of compressed sensing, but the solution requires iteration, which consumes a lot of time and only solves the reconstruction for a single image. At present, the more advanced image restoration methods are based on convolutional neural networks, but few people have tried to apply Transformer to the field of medical image reconstruction and have shown relatively good results. In this article, we propose a magnetic resonance imaging reconstruction model SMIR based on Swin Transformers, namely SMIR. SMIR consists of two modules: a multi-level feature extraction module and a reconstruction module. The model combines frequency domain and spatial domain losses to better reconstruct image details. We compared this model with some traditional image processing methods and advanced convolutional neural networks image restoration methods. The experimental results show that our method achieves the best results.

 
重要日期

  • 会议日期

    11月13日

    2021

    至

    11月14日

    2021

  • 09月30日 2021

    报告提交截止日期

  • 11月14日 2021

    注册截止日期

主办单位
IEEE北京分会
中国生物医学工程学会医学物理分会
中国电子学会生命电子学分会
承办单位
中国科学技术大学
安徽省生物医学工程学会
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