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Accelerated Simultaneous Multi-Slice MRI using Subject-Specific Convolutional Neural Networks

Authors
  • Zhang, Chi1, 2
  • Moeller, Steen2
  • Weingärtner, Sebastian1, 2, 3
  • Uğurbil, Kâmil2
  • Akçakaya, Mehmet1, 2
  • 1 Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN
  • 2 Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN
  • 3 Computer Assisted Clinical Medicine, University Hospital Mannheim, Heidelberg University, Heidelberg, Germany
Type
Published Article
Journal
Conference record. Asilomar Conference on Signals, Systems & Computers
Publication Date
Oct 01, 2018
Volume
2018
Pages
1636–1640
Identifiers
DOI: 10.1109/ACSSC.2018.8645313
PMID: 31892767
PMCID: PMC6938220
Source
PubMed Central
License
Unknown
External links

Abstract

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.

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