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Magn Reson Imaging. 2019 Jun 24. pii: S0730-725X(18)30650-7. doi: 10.1016/j.mri.2019.05.038. [Epub ahead of print]

Applications of a deep learning method for anti-aliasing and super-resolution in MRI.

Author information

1
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA. Electronic address: czhao20@jhu.edu.
2
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.
3
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
4
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA; Kirby Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
5
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA; Department of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland.
6
Department of Radiology, Harvard Medical School, Boston, MA, USA; Massachusetts General Hospital, Boston, MA, USA.
7
Johns Hopkins University School of Medicine, Baltimore, MD, USA.
8
Department of Neural and Pain Sciences, University of Maryland, Baltimore, MD, USA.
9
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA; Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Abstract

Magnetic resonance (MR) images with both high resolutions and high signal-to-noise ratios (SNRs) are desired in many clinical and research applications. However, acquiring such images takes a long time, which is both costly and susceptible to motion artifacts. Acquiring MR images with good in-plane resolution and poor through-plane resolution is a common strategy that saves imaging time, preserves SNR, and provides one viewpoint with good resolution in two directions. Unfortunately, this strategy also creates orthogonal viewpoints that have poor resolution in one direction and, for 2D MR acquisition protocols, also creates aliasing artifacts. A deep learning approach called SMORE that carries out both anti-aliasing and super-resolution on these types of acquisitions using no external atlas or exemplars has been previously reported but not extensively validated. This paper reviews the SMORE algorithm and then demonstrates its performance in four applications with the goal to demonstrate its potential for use in both research and clinical scenarios. It is first shown to improve the visualization of brain white matter lesions in FLAIR images acquired from multiple sclerosis patients. Then it is shown to improve the visualization of scarring in cardiac left ventricular remodeling after myocardial infarction. Third, its performance on multi-view images of the tongue is demonstrated and finally it is shown to improve performance in parcellation of the brain ventricular system. Both visual and selected quantitative metrics of resolution enhancement are demonstrated.

KEYWORDS:

Aliasing; Deep learning; MRI; Reconstruction; SMORE; Segmentation; Super-resolution

PMID:
31247254
DOI:
10.1016/j.mri.2019.05.038

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