Artificial Neural Network for Suppression of Banding Artifacts in Balanced Steady-State Free Precession MRI

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This study is the first attempt for a learning-based algorithm to be applied to banding artifact suppression in balanced steady-state free precession (bSSFP). We trained multilayer perceptron (MLP) models with two or four phase‑cycling datasets and banding-free datasets as inputs and outputs, respectively. We demonstrated that MLP was superior to existing methods in terms of banding artifact suppression and SNR efficiency, which was clearer in two phase‑cycling datasets. Furthermore, MLP was widely applicable to various image sets, irrespective of scan parameters, body organs, and field strengths. The learning-based approach is promising for banding artifact suppression of bSSFP.
Publisher
International Society for Magnetic Resonance in Medicine
Issue Date
2017-04-25
Language
English
Citation

International Society for Magnetic Resonance in Medicine 2017, pp.3982

URI
http://hdl.handle.net/10203/223815
Appears in Collection
BiS-Conference Papers(학술회의논문)
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