Keywords: Machine Learning/Artificial Intelligence, Image Reconstruction, Super Resolution, BLADE sequence
Motivation: Conventional super-resolution techniques are not applicable to magnetic resonance images reconstructed from BLADE sequences, where four corners of the k-space are missing.
Goal(s): When BLADE data are fed into a common super-resolution model targeting ordinary Cartesian k-space data, strong Gibbs rings appear due to the truncation of k-space. On this basis, we propose a deep learning-based method specifically for the super-resolution task with BLADE data.
Approach: We mainly used the Residual Density Network (RDN) and designed the downsampling method based on the characteristics of BLADE data.
Results: Experimental results show that our model is able to predict high-resolution MR images with fewer artifacts.
Impact: By applying our RDN-based model specifically adapted to BLADE data, the image matrix size can be increased by a factor of 2 to produce sharper images without increasing acquisition time.
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