Journal article
DeepSENSE: Learning coil sensitivity functions for SENSE reconstruction using deep learning
Magnetic resonance in medicine, Vol.87(4), pp.1894-1902
04/2022
DOI: 10.1002/mrm.29085
PMCID: PMC8810692
PMID: 34825732
Abstract
Purpose To improve the estimation of coil sensitivity functions from limited auto-calibration signals (ACS) in SENSE-based reconstruction for brain imaging. Methods We propose to use deep learning to estimate coil sensitivity functions by leveraging information from previous scans obtained using the same RF receiver system. Specifically, deep convolutional neural networks were designed to learn an end-to-end mapping from the initial sensitivity to the high-resolution counterpart. Sensitivity alignment was further proposed to reduce the geometric variation caused by different subject positions and imaging FOVs. Cross-validation with a small set of datasets was performed to validate the learned neural network. Iterative SENSE reconstruction was adopted to evaluate the utility of the sensitivity functions from the proposed and conventional methods. Results The proposed method produced improved sensitivity estimates and SENSE reconstructions compared to the conventional methods in terms of aliasing and noise suppression with very limited ACS data. Cross-validation with a small set of data demonstrated the feasibility of learning coil sensitivity functions for brain imaging. The network learned on the spoiled GRE data can be applied to predict sensitivity functions for spin-echo and MPRAGE datasets. Conclusion A deep learning-based method has been proposed for improving the estimation of coil sensitivity functions. Experimental results have demonstrated the feasibility and potential of the proposed method for improving SENSE-based reconstructions especially when the ACS data are limited.
Details
- Title: Subtitle
- DeepSENSE: Learning coil sensitivity functions for SENSE reconstruction using deep learning
- Creators
- Xi Peng - Mayo Clin, Dept Radiol, Rochester, MN 55905 USABradley P. Sutton - University of Illinois Urbana-ChampaignFan Lam - University of Illinois Urbana-ChampaignZhi-Pei Liang - University of Illinois Urbana-Champaign
- Resource Type
- Journal article
- Publication Details
- Magnetic resonance in medicine, Vol.87(4), pp.1894-1902
- DOI
- 10.1002/mrm.29085
- PMID
- 34825732
- PMCID
- PMC8810692
- NLM abbreviation
- Magn Reson Med
- ISSN
- 0740-3194
- eISSN
- 1522-2594
- Publisher
- Wiley
- Number of pages
- 9
- Grant note
- P41-EB002034; R21-EB021013-01; 1R21EB029076A / Foundation for the National Institutes of Health; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA 1944249 / National Science Foundation; National Science Foundation (NSF)
- Language
- English
- Date published
- 04/2022
- Academic Unit
- Radiology
- Record Identifier
- 9984446267202771
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