Deep learning-based super-resolution of 3D magnetic resonance images by regularly spaced shifting

被引：9

作者：

Thurnhofer-Hemsi, Karl ^{[1
]}

Lopez-Rubio, Ezequiel ^{[1
]}

Dominguez, Enrique ^{[1
]}

Marcos Luque-Baena, Rafael ^{[1
]}

Roe-Vellve, Nuria ^{[2
]}

机构：

[1] Univ Malaga, Dept Comp Languages & Comp Sci, Bulevar Louis Pasteur 35, Malaga 29071, Spain

[2] Univ Malaga, Mol Imaging Unit, Ctr Invest Med, Sanitarias Gen Fdn, C Marques de Beccaria 3, Malaga 29010, Spain

来源：

NEUROCOMPUTING | 2020年 / 398卷

关键词：

Magnetic resonance imaging; Super resolution; Convolutional neural networks; Supervised learning;

D O I：

10.1016/j.neucom.2019.05.107

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The image acquisition process in the field of magnetic resonance imaging (MRI) does not always provide high resolution results that may be useful for a clinical analysis. Super-resolution (SR) techniques manage to increase the image resolution, being especially effective those based on examples that determine a correspondence between patterns of low resolution and high resolution. Deep learning neural networks have been applied in recent years to estimate this association with very competitive results. In this work, the starting point is a convolutional neuronal network to which a regularly spaced shifting mechanism over the input image is applied, with the aim of substantially improving the quality of the resulting image. This hybrid proposal has been compared with several SR techniques using the peak signal-to-noise ratio, structural similarity index and Bhattacharyya coefficient metrics. The results obtained on different MR images show a considerable improvement both in the restored image and in the residual image without an excessive increase in computing time. (C) 2019 Elsevier B.V. All rights reserved.

引用

页码：314 / 327

页数：14

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