Super-Resolution Network with Information Distillation and Multi-Scale Attention for Medical CT Image release_x64ockympndy3duyh3bzkzavcy

by Tianliu Zhao, Lei Hu, Yongmei Zhang, Jianying Fang

Published in Sensors by MDPI AG.

2021   Volume 21, Issue 20, p6870

Abstract

The CT image is an important reference for clinical diagnosis. However, due to the external influence and equipment limitation in the imaging, the CT image often has problems such as blurring, a lack of detail and unclear edges, which affect the subsequent diagnosis. In order to obtain high-quality medical CT images, we propose an information distillation and multi-scale attention network (IDMAN) for medical CT image super-resolution reconstruction. In a deep residual network, instead of only adding the convolution layer repeatedly, we introduce information distillation to make full use of the feature information. In addition, in order to better capture information and focus on more important features, we use a multi-scale attention block with multiple branches, which can automatically generate weights to adjust the network. Through these improvements, our model effectively solves the problems of insufficient feature utilization and single attention source, improves the learning ability and expression ability, and thus can reconstruct the higher quality medical CT image. We conduct a series of experiments; the results show that our method outperforms the previous algorithms and has a better performance of medical CT image reconstruction in the objective evaluation and visual effect.
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Type  article-journal
Stage   published
Date   2021-10-16
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DOI  10.3390/s21206870
PubMed  34696083
PMC  PMC8539557
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