Fusing multi-scale information in convolution network for MR image super-resolution reconstruction

Biomedical Engineering Online
Chang LiuJiLiu Zhou

Abstract

Magnetic resonance (MR) images are usually limited by low spatial resolution, which leads to errors in post-processing procedures. Recently, learning-based super-resolution methods, such as sparse coding and super-resolution convolution neural network, have achieved promising reconstruction results in scene images. However, these methods remain insufficient for recovering detailed information from low-resolution MR images due to the limited size of training dataset. To investigate the different edge responses using different convolution kernel sizes, this study employs a multi-scale fusion convolution network (MFCN) to perform super-resolution for MRI images. Unlike traditional convolution networks that simply stack several convolution layers, the proposed network is stacked by multi-scale fusion units (MFUs). Each MFU consists of a main path and some sub-paths and finally fuses all paths within the fusion layer. We discussed our experimental network parameters setting using simulated data to achieve trade-offs between the reconstruction performance and computational efficiency. We also conducted super-resolution reconstruction experiments using real datasets of MR brain images and demonstrated that the proposed MFCN has achiev...Continue Reading

References

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Citations

Jun 9, 2020·Topics in Magnetic Resonance Imaging : TMRI·Xuan V NguyenLuciano M Prevedello
Dec 1, 2018·Japanese Journal of Radiology·Toru HigakiKazuo Awai
Feb 16, 2020·Academic Radiology·Michael L RichardsonKali Xu
Jul 3, 2021·Scientific Reports·Jared HamwoodDavid Alonso-Caneiro

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