论文标题
大脑的精细感知剂MR图像超分辨率在小波域中
Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain
论文作者
论文摘要
磁共振成像在计算机辅助诊断和大脑勘探中起重要作用。但是，受硬件，扫描时间和成本的限制，在临床上获取高分辨率（HR）磁共振（MR）图像是一项挑战。在本文中，提出了良好的感知生成对抗网络（FP-GAN）来产生来自低分辨率对应物的HR MR图像。它可以以分裂和构成方式应对现有超分辨率模型的细节不敏感问题。具体而言，FP-GAN首先将MR图像分为小波域中的低频全局近似和高频解剖纹理。然后，每个子频段生成对抗网络（子频段gan）征服了每个单个子频段图像的超分辨率过程。同时，部署了子带的注意力，以调整全球和纹理信息之间的重点。它可以专注于子频段图像，而不是特征图，以进一步增强FP-GAN的解剖重建能力。此外，将反离散小波转换（IDWT）集成到用于考虑整个图像的重建的模型中。在Multires_7t数据集上的实验表明，FP-GAN的表现在数量和质量上优于竞争方法。
Magnetic resonance imaging plays an important role in computer-aided diagnosis and brain exploration. However, limited by hardware, scanning time and cost, it's challenging to acquire high-resolution (HR) magnetic resonance (MR) image clinically. In this paper, fine perceptive generative adversarial networks (FP-GANs) is proposed to produce HR MR images from low-resolution counterparts. It can cope with the detail insensitive problem of the existing super-resolution model in a divide-and-conquer manner. Specifically, FP-GANs firstly divides an MR image into low-frequency global approximation and high-frequency anatomical texture in wavelet domain. Then each sub-band generative adversarial network (sub-band GAN) conquers the super-resolution procedure of each single sub-band image. Meanwhile, sub-band attention is deployed to tune focus between global and texture information. It can focus on sub-band images instead of feature maps to further enhance the anatomical reconstruction ability of FP-GANs. In addition, inverse discrete wavelet transformation (IDWT) is integrated into model for taking the reconstruction of whole image into account. Experiments on MultiRes_7T dataset demonstrate that FP-GANs outperforms the competing methods quantitatively and qualitatively.
