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Real‐time cardiovascular MR with spatio‐temporal artifact suppression using deep learning–proof of concept in congenital heart disease

PURPOSE: Real‐time assessment of ventricular volumes requires high acceleration factors. Residual convolutional neural networks (CNN) have shown potential for removing artifacts caused by data undersampling. In this study, we investigated the ability of CNNs to reconstruct highly accelerated radial...

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Autores principales: Hauptmann, Andreas, Arridge, Simon, Lucka, Felix, Muthurangu, Vivek, Steeden, Jennifer A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6492123/
https://www.ncbi.nlm.nih.gov/pubmed/30194880
http://dx.doi.org/10.1002/mrm.27480
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author Hauptmann, Andreas
Arridge, Simon
Lucka, Felix
Muthurangu, Vivek
Steeden, Jennifer A.
author_facet Hauptmann, Andreas
Arridge, Simon
Lucka, Felix
Muthurangu, Vivek
Steeden, Jennifer A.
author_sort Hauptmann, Andreas
collection PubMed
description PURPOSE: Real‐time assessment of ventricular volumes requires high acceleration factors. Residual convolutional neural networks (CNN) have shown potential for removing artifacts caused by data undersampling. In this study, we investigated the ability of CNNs to reconstruct highly accelerated radial real‐time data in patients with congenital heart disease (CHD). METHODS: A 3D (2D plus time) CNN architecture was developed and trained using synthetic training data created from previously acquired breath hold cine images from 250 CHD patients. The trained CNN was then used to reconstruct actual real‐time, tiny golden angle (tGA) radial SSFP data (13 × undersampled) acquired in 10 new patients with CHD. The same real‐time data was also reconstructed with compressed sensing (CS) to compare image quality and reconstruction time. Ventricular volume measurements made using both the CNN and CS reconstructed images were compared to reference standard breath hold data. RESULTS: It was feasible to train a CNN to remove artifact from highly undersampled radial real‐time data. The overall reconstruction time with the CNN (including creation of aliased images) was shown to be >5 × faster than the CS reconstruction. In addition, the image quality and accuracy of biventricular volumes measured from the CNN reconstructed images were superior to the CS reconstructions. CONCLUSION: This article has demonstrated the potential for the use of a CNN for reconstruction of real‐time radial data within the clinical setting. Clinical measures of ventricular volumes using real‐time data with CNN reconstruction are not statistically significantly different from gold‐standard, cardiac‐gated, breath‐hold techniques.
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spelling pubmed-64921232019-05-06 Real‐time cardiovascular MR with spatio‐temporal artifact suppression using deep learning–proof of concept in congenital heart disease Hauptmann, Andreas Arridge, Simon Lucka, Felix Muthurangu, Vivek Steeden, Jennifer A. Magn Reson Med Full Papers—Imaging Methodology PURPOSE: Real‐time assessment of ventricular volumes requires high acceleration factors. Residual convolutional neural networks (CNN) have shown potential for removing artifacts caused by data undersampling. In this study, we investigated the ability of CNNs to reconstruct highly accelerated radial real‐time data in patients with congenital heart disease (CHD). METHODS: A 3D (2D plus time) CNN architecture was developed and trained using synthetic training data created from previously acquired breath hold cine images from 250 CHD patients. The trained CNN was then used to reconstruct actual real‐time, tiny golden angle (tGA) radial SSFP data (13 × undersampled) acquired in 10 new patients with CHD. The same real‐time data was also reconstructed with compressed sensing (CS) to compare image quality and reconstruction time. Ventricular volume measurements made using both the CNN and CS reconstructed images were compared to reference standard breath hold data. RESULTS: It was feasible to train a CNN to remove artifact from highly undersampled radial real‐time data. The overall reconstruction time with the CNN (including creation of aliased images) was shown to be >5 × faster than the CS reconstruction. In addition, the image quality and accuracy of biventricular volumes measured from the CNN reconstructed images were superior to the CS reconstructions. CONCLUSION: This article has demonstrated the potential for the use of a CNN for reconstruction of real‐time radial data within the clinical setting. Clinical measures of ventricular volumes using real‐time data with CNN reconstruction are not statistically significantly different from gold‐standard, cardiac‐gated, breath‐hold techniques. John Wiley and Sons Inc. 2018-09-08 2019-02 /pmc/articles/PMC6492123/ /pubmed/30194880 http://dx.doi.org/10.1002/mrm.27480 Text en © 2018 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Full Papers—Imaging Methodology
Hauptmann, Andreas
Arridge, Simon
Lucka, Felix
Muthurangu, Vivek
Steeden, Jennifer A.
Real‐time cardiovascular MR with spatio‐temporal artifact suppression using deep learning–proof of concept in congenital heart disease
title Real‐time cardiovascular MR with spatio‐temporal artifact suppression using deep learning–proof of concept in congenital heart disease
title_full Real‐time cardiovascular MR with spatio‐temporal artifact suppression using deep learning–proof of concept in congenital heart disease
title_fullStr Real‐time cardiovascular MR with spatio‐temporal artifact suppression using deep learning–proof of concept in congenital heart disease
title_full_unstemmed Real‐time cardiovascular MR with spatio‐temporal artifact suppression using deep learning–proof of concept in congenital heart disease
title_short Real‐time cardiovascular MR with spatio‐temporal artifact suppression using deep learning–proof of concept in congenital heart disease
title_sort real‐time cardiovascular mr with spatio‐temporal artifact suppression using deep learning–proof of concept in congenital heart disease
topic Full Papers—Imaging Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6492123/
https://www.ncbi.nlm.nih.gov/pubmed/30194880
http://dx.doi.org/10.1002/mrm.27480
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