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A hybrid convolutional neural network for super‐resolution reconstruction of MR images
Author(s) -
Zheng Yingjie,
Zhen Bowen,
Chen Aichi,
Qi Fulang,
Hao Xiaohan,
Qiu Bensheng
Publication year - 2020
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.14152
Subject(s) - convolutional neural network , computer science , peak signal to noise ratio , artificial intelligence , image quality , image resolution , pattern recognition (psychology) , iterative reconstruction , block (permutation group theory) , similarity (geometry) , signal to noise ratio (imaging) , artificial neural network , image (mathematics) , noise (video) , computer vision , algorithm , mathematics , telecommunications , geometry
Purpose 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. Methods 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. Results 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. Conclusions 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.