Optimization of Regularization Parameter for GRAPPA Reconstruction
نویسندگان
چکیده
The effectiveness of regularization to improve SNR in parallel imaging techniques has been reported in previous works [1-2], but how to optimize the regularization parameter remains a problem. The regularization parameter controls the degree of regularization and thereby determines the compromise between SNR and artifacts. Over-regularization causes high level of artifact, while under-regularization can not prevent noise amplification. In this study, three regularization parameter choice strategies are compared in GRAPPA reconstruction: the L-curve method, the fixed singular value (SV) threshold method, and a novel discrepancy principle approach. In vivo experiment results show that the discrepancy-based parameter choice strategy significantly outperforms the others. It can automatically choose nearly optimal parameters for the reconstructions so as to achieve good compromise between SNR and artifacts. Method
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