Keywords: Image Reconstruction, Image Reconstruction
Self-Calibrating GROG (SC-GROG) is a gridding algorithm that maps the k-space MRI data from non-Cartesian to Cartesian domain. The main limitation of SC-GROG is its computational cost to calculate the GROG weights. This paper proposes a customized deep learning framework (based on VGG-16 CNN model) to calculate the 2D-Gridding weight sets for SC-GROG. Initially, the proposed model is trained on human head images, and later fine-tuning is performed using Golden-angle radial Liver Perfusion datasets. The results show that the proposed method significantly reduces the computation time for the estimation of GROG weights while maintaining the image quality.
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