Simultaneous multi-slice or multi-band (SMS/MB) imaging allows accelerated coverage in magnetic resonance imaging (MRI). Multiple slices are excited and acquired at the same time, and reconstructed using the redundancies in receiver coil arrays, similar to parallel imaging. SMS/MB reconstruction is currently performed with linear reconstruction techniques. Recently, a nonlinear reconstruction method for parallel imaging, Robust Artificial-neural-networks for k-space Interpolation (RAKI) was proposed and shown to improve upon linear methods. This method uses convolutional neural networks (CNN) trained solely on subject-specific calibration data. In this study, we sought to extend RAKI to SMS/MB imaging reconstruction. CNN training was performed on calibration data acquired prior to SMS/MB imaging, in a manner consistent with the existing linear methods. These CNNs were used to reconstruct a time series of functional MRI (fMRI) data. CNN network parameters were optimized using an extensive search of the parameter space. With these optimal parameters, RAKI substantially improves image quality compared to a commonly used linear reconstruction algorithm, especially for high acceleration rates.
|Original language||English (US)|
|Title of host publication||Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018|
|Editors||Michael B. Matthews|
|Publisher||IEEE Computer Society|
|Number of pages||5|
|State||Published - Feb 19 2019|
|Event||52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 - Pacific Grove, United States|
Duration: Oct 28 2018 → Oct 31 2018
|Name||Conference Record - Asilomar Conference on Signals, Systems and Computers|
|Conference||52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018|
|Period||10/28/18 → 10/31/18|
Bibliographical noteFunding Information:
This work was partially supported by NIH R00HL111410, NIH U01EB025144, NIH P41EB015894, NSF CAREER CCF-1651825.
© 2018 IEEE.