Split-slice training and hyperparameter tuning of RAKI networks for simultaneous multi-slice reconstruction. Magn Reson Med 2021 Jun;85(6):3272-3280
Date
12/18/2020Pubmed ID
33331002DOI
10.1002/mrm.28634Scopus ID
2-s2.0-85097598109 (requires institutional sign-in at Scopus site) 6 CitationsAbstract
PURPOSE: 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.
METHODS: RAKI networks were developed with variable hyperparameters and with and without split-slice training data generation. Each network was trained and applied to five different datasets including acquisitions harmonized with Human Connectome Project lifespan protocol. Unaliasing performance was assessed through L1 errors computed between unaliased and calibration frequency-space data.
RESULTS: Split-slice training significantly improved network performance in nearly all hyperparameter configurations. Best unaliasing results were achieved with three layer RAKI networks using at least 64 convolutional filters with receptive fields of 7 voxels, 128 single-voxel filters in the penultimate RAKI layer, batch normalization, and no training dropout with the split-slice augmented training dataset. Networks trained without the split-slice technique showed symptoms of network over-fitting.
CONCLUSIONS: Split-slice training for simultaneous multi-slice RAKI networks positively impacts network performance. Hyperparameter tuning of such reconstruction networks can lead to further improvements in unaliasing performance.
Author List
Nencka AS, Arpinar VE, Bhave S, Yang B, Banerjee S, McCrea M, Mickevicius NJ, Muftuler LT, Koch KMAuthors
Kevin M. Koch PhD Center Director, Professor in the Radiology department at Medical College of WisconsinMichael McCrea PhD Professor in the Neurosurgery department at Medical College of Wisconsin
Nikolai J. Mickevicius PhD Assistant Professor in the Biophysics department at Medical College of Wisconsin
Lutfi Tugan Muftuler PhD Professor in the Neurosurgery department at Medical College of Wisconsin
Andrew S. Nencka PhD Director, Associate Professor in the Radiology department at Medical College of Wisconsin
MESH terms used to index this publication - Major topics in bold
CalibrationHumans
Image Processing, Computer-Assisted
