Open AccessNetwork slimming for compressed‐sensing cardiac cine MRI
Author(s) -
Park Seong Jae,
Lim Chae Guk,
Ahn ChangBeom
Publication year - 2021
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/ell2.12084
Subject(s) - upsampling , convolution (computer science) , compressed sensing , transpose , reduction (mathematics) , computer science , degradation (telecommunications) , artificial intelligence , algorithm , computer vision , mathematics , image (mathematics) , artificial neural network , telecommunications , physics , eigenvalues and eigenvectors , geometry , quantum mechanics
The Unet is attempted to be slimmed for compressed‐sensing cardiac cine magnetic resonance imaging. Despite the excellent performance of the U‐net, its heavy structure and long training time restrict its applications in an environment with limited computational resources. We slimmed the U‐net by changing the multiple convolutions to single convolution and the transpose convolution to upsampling and by adopting fewer layers, without performance degradation. The number of trainable weights and training time of the slimmed network was reduced by 87.9% and 48.1%, respectively. The proposed network showed improved performance with a 1.45% reduction in the normalised mean square error.