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  4. Vol 3, Issue 3, 2023

Magnetic Resonance Image Super-Resolution via Multi-Resolution Learning

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DOI: 10.23977/phpm.2023.030307 | Downloads: 10 | Views: 374

Author(s)

Wang Wentao 1, Zhang Kaixiang 1

Affiliation(s)

1 Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China

Corresponding Author

Wang Wentao

ABSTRACT

High-resolution magnetic resonance images are of great significance for medical diagnosis. A convolutional neural network with multi-resolution learning is proposed for magnetic resonance image (MR) superresolution. The network is an improved deep residual network, which involves residual units for feature extraction, a deconvolution layer for multi-resolution up-sampling, and a multi-resolution learning layer. The proposed network performs the super-resolution task in the low-resolution space, which can accelerate the network. Multiresolution upsampling is put forward to integrate multiple residual unit information and to accelerate the network. Multi-resolution learning can adaptively determine the contributions of these upsampled high-dimensional feature maps to high-resolution MR image reconstruction. Experiment results indicate that the proposed method can achieve a good super-resolution reconstruction performance for magnetic resonance images, which is superior to the state-of-the-art deep learning methods.

KEYWORDS

Convolutional neural network; multi-resolution learning; magnetic resonance image; super-resolution reconstruction

CITE THIS PAPER

Wang Wentao, Zhang Kaixiang, Magnetic Resonance Image Super-Resolution via Multi-Resolution Learning. MEDS Public Health and Preventive Medicine (2023) Vol. 3: 43-51. DOI: http://dx.doi.org/10.23977/phpm.2023.030307.

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