Split‐slice training and hyperparameter tuning of RAKI networks for simultaneous multi‐slice reconstruction

Abstract

Simultaneous multi‐slice acquisitions are essential for modern neuroimaging research, enabling high temporal resolution functional and high‐resolution q‐space sampling diffusion acquisitions. Recently, deep learning reconstruction techniques have been introduced for unaliasing these accelerated acquisitions, and robust artificial‐neural‐networks for k‐space interpolation (RAKI) have shown promising capabilities. This study systematically examines the impacts of hyperparameter selections for RAKI networks, and introduces a novel technique for training data generation which is analogous to the split‐slice formalism used in slice‐GRAPPA.