RAKI is a scan-specific k-space interpolation technique based on deep convolutional networks, which bears superior noise resilience compared to GRAPPA. However, RAKI may introduce severe blurring in image reconstruction due to reduced number of autocalibration signal lines at higher acceleration factors. We propose Gradual RAKI, which exhibits the benefit of mixing RAKI and GRAPPA in a preparatory block for data augmentation purposes prior to a conventional RAKI reconstruction. Data augmentation provides an effective way to create synthetic ACS lines out of 8 original ACS lines at 4-fold acceleration, while valuable features are retained from both RAKI and GRAPPA reconstruction methods.
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