DeepSENSE: Learning coil sensitivity functions for SENSE reconstruction using deep learning.

Journal: Magnetic resonance in medicine

Published Date: Nov 26, 2021

Abstract

PURPOSE: To improve the estimation of coil sensitivity functions from limited auto-calibration signals (ACS) in SENSE-based reconstruction for brain imaging.

Authors

Xi Peng

Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Bradley P Sutton

Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.
Fan Lam

Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.
Zhi-Pei Liang

Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.

Keywords

Algorithms Brain Deep Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging Neural Networks, Computer

External Resources

View on PubMed Access via DOI PubMed (34825732)

DeepSENSE: Learning coil sensitivity functions for SENSE reconstruction using deep learning.

Abstract

Authors

Keywords

External Resources

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