Abstract
High-resolution magnetic resonance images can provide fine-grained anatomical information, but acquiring such data requires a long scanning time. In this paper, a framework called the Fused Attentive Generative Adversarial Networks(FA-GAN) is proposed to generate the super- resolution MR image from low-resolution magnetic resonance images, which can reduce the scanning time effectively but with high resolution MR images. In the framework of the FA-GAN, the local fusion feature block, consisting of different three-pass networks by using different convolution kernels, is proposed to extract image features at different scales. And the global feature fusion module, including the channel attention module, the self-attention module, and the fusion operation, is designed to enhance the important features of the MR image. Moreover, the spectral normalization process is introduced to make the discriminator network stable. 40 sets of 3D magnetic resonance images (each set of images contains 256 slices) are used to train the network, and 10 sets of images are used to test the proposed method. The experimental results show that the PSNR and SSIM values of the super-resolution magnetic resonance image generated by the proposed FA-GAN method are higher than the state-of-the-art reconstruction methods.
Highlights
Image super-resolution refers to the reconstruction of high-resolution images from low-resolution images [1]
To ensure the fairness of the test, we conducted two independent tests to verify the performance of the proposed FA-Generative Adversarial Network (GAN) model
The fusion attention based generative adversarial networks (FA-GAN) networks were trained with a learning rate of 0.0001.The model training takes 10 hours at a time
Summary
Image super-resolution refers to the reconstruction of high-resolution images from low-resolution images [1]. High resolution means that the pixels in the image are denser and can display more flexible details [2][3]. These details are very useful in practical applications, such as satellite imaging, medical imaging, etc, which can better identify targets and find important features in high-resolution images [4,5,6]. High-resolution (HR) MRI images can provide fine anatomical information, which is helpful for clinical diagnosis and accurate decision-making[7][8]. It requires expensive equipment and requires a long scanning time, which brings challenges to image data acquisition. Further applications are limited by slow data acquiring and imaging speed[9][10]
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