论文标题
灵活的多对比度MRI的灵活比对超分辨率网络
Flexible Alignment Super-Resolution Network for Multi-Contrast MRI
论文作者
论文摘要
通过获取生物组织的结构信息，磁共振成像在临床诊断中起着至关重要的作用。最近，许多多对比度MRI超分辨率网络都能实现良好的效果。但是，大多数研究都忽略了不适当的前景尺度和多对比度MRI的斑块大小的影响，这可能会导致不适当的特征对齐。为了解决这个问题，我们提出了用于多对比度MRI超分辨率的柔性比对超分辨率网络（FASR-NET）。 FASR-NET的柔性比对模块由两个用于特征比对的模块组成。 （1）单人金字塔比对（S-A）模块求解了低分辨率（LR）图像和参考图像（REF）图像具有不同尺度的情况。 （2）多阵行金字塔比对（M-A）模块求解了LR和REF图像具有相同量表的情况。此外，我们提出了跨层次渐进式融合（CHPF）模块，旨在有效地融合功能，从而进一步提高图像质量。与其他最先进的方法相比，FASR-NET在FastMRI和IXI数据集上取得了最具竞争力的结果。我们的代码将在\ href {https://github.com/yimingliu123/fasr-net} {https://github.com/yimingliu123/fasr-net}中获得。
Magnetic resonance imaging plays an essential role in clinical diagnosis by acquiring the structural information of biological tissue. Recently, many multi-contrast MRI super-resolution networks achieve good effects. However, most studies ignore the impact of the inappropriate foreground scale and patch size of multi-contrast MRI, which probably leads to inappropriate feature alignment. To tackle this problem, we propose the Flexible Alignment Super-Resolution Network (FASR-Net) for multi-contrast MRI Super-Resolution. The Flexible Alignment module of FASR-Net consists of two modules for feature alignment. (1) The Single-Multi Pyramid Alignment(S-A) module solves the situation where low-resolution (LR) images and reference (Ref) images have different scales. (2) The Multi-Multi Pyramid Alignment(M-A) module solves the situation where LR and Ref images have the same scale. Besides, we propose the Cross-Hierarchical Progressive Fusion (CHPF) module aiming at fusing the features effectively, further improving the image quality. Compared with other state-of-the-art methods, FASR-net achieves the most competitive results on FastMRI and IXI datasets. Our code will be available at \href{https://github.com/yimingliu123/FASR-Net}{https://github.com/yimingliu123/FASR-Net}.
