Residual RAKI: A hybrid linear and non-linear approach for scan-specific k-space deep learning.

ABSTRACT: Parallel imaging is the most clinically used acceleration technique for magnetic resonance imaging (MRI) in part due to its easy inclusion into routine acquisitions. In k-space based parallel imaging reconstruction, sub-sampled k-space data are interpolated using linear convolutions. At high acceleration rates these methods have inherent noise amplification and reduced image quality. On the other hand, non-linear deep learning methods provide improved image quality at high acceleration, but the availability of training databases for different scans, as well as their interpretability hinder their adaptation. In this work, we present an extension of Robust Artificial-neural-networks for k-space Interpolation (RAKI), called residual-RAKI (rRAKI), which achieves scan-specific machine learning

SUBMITTER: Zhang C 

PROVIDER: S-EPMC9179026 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature