A hybrid convolutional neural network for super-resolution reconstruction of MR images

Spatial resolution is an important parameter for magnetic resonance imaging (MRI). High-resolution MR images provide detailed information and benefit subsequent image analysis. However, higher resolution MR images come at the expense of longer scanning time and lower signal-to-noise ratios (SNRs). Using algorithms to improve image resolution can mitigate these limitations. Recently, some convolutional neural network (CNN)-based super-resolution (SR) algorithms have flourished on MR image reconstruction. However, most algorithms usually adopt deeper network structures to improve the performance.

In this study, we propose a novel hybrid network (named HybridNet) to improve the quality of SR images by increasing the width of the network. Specifically, the proposed hybrid block combines a multipath structure and variant dense blocks to extract abundant features from low-resolution images. Furthermore, we fully exploit the hierarchical features from different hybrid blocks to reconstruct high-quality images.

All SR algorithms are evaluated using three MR image datasets and the proposed HybridNet outperformed the comparative methods with peak a signal-to-noise ratio (PSNR) of 42.12 ± 0.92 dB, 38.60 ± 2.46 dB, 35.17 ± 2.96 dB and a structural similarity index (SSIM) of 0.9949 ± 0.0015, 0.9892 ± 0.0034, 0.9740 ± 0.0064, respectively. Besides, our proposed network can reconstruct high-quality images on an unseen MR dataset with PSNR of 33.27 ± 1.56 and SSIM of 0.9581 ± 0.0068.

The results demonstrate that HybridNet can reconstruct high-quality SR images from degraded MR images and has good generalization ability. It also can be leveraged to assist the task of image analysis or processing.