Motion Artifact Reduction Using a Convolutional Neural Network for Dynamic Contrast Enhanced MR Imaging of the Liver

PURPOSE: To improve the quality of images obtained via dynamic contrast enhanced MRI (DCE-MRI), which contain motion artifacts and blurring using a deep learning approach. MATERIALS AND METHODS: A multi-channel convolutional neural network-based method is proposed for reducing the motion artifacts a...

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Autores principales: Tamada, Daiki, Kromrey, Marie-Luise, Ichikawa, Shintaro, Onishi, Hiroshi, Motosugi, Utaroh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Japanese Society for Magnetic Resonance in Medicine 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7067907/
https://www.ncbi.nlm.nih.gov/pubmed/31061259
http://dx.doi.org/10.2463/mrms.mp.2018-0156
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author Tamada, Daiki
Kromrey, Marie-Luise
Ichikawa, Shintaro
Onishi, Hiroshi
Motosugi, Utaroh
author_facet Tamada, Daiki
Kromrey, Marie-Luise
Ichikawa, Shintaro
Onishi, Hiroshi
Motosugi, Utaroh
author_sort Tamada, Daiki
collection PubMed
description PURPOSE: To improve the quality of images obtained via dynamic contrast enhanced MRI (DCE-MRI), which contain motion artifacts and blurring using a deep learning approach. MATERIALS AND METHODS: A multi-channel convolutional neural network-based method is proposed for reducing the motion artifacts and blurring caused by respiratory motion in images obtained via DCE-MRI of the liver. The training datasets for the neural network included images with and without respiration-induced motion artifacts or blurring, and the distortions were generated by simulating the phase error in k-space. Patient studies were conducted using a multi-phase T(1)-weighted spoiled gradient echo sequence for the liver, which contained breath-hold failures occurring during data acquisition. The trained network was applied to the acquired images to analyze the filtering performance, and the intensities and contrast ratios before and after denoising were compared via Bland–Altman plots. RESULTS: The proposed network was found to be significantly reducing the magnitude of the artifacts and blurring induced by respiratory motion, and the contrast ratios of the images after processing via the network were consistent with those of the unprocessed images. CONCLUSION: A deep learning-based method for removing motion artifacts in images obtained via DCE-MRI of the liver was demonstrated and validated.
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spelling pubmed-70679072020-03-19 Motion Artifact Reduction Using a Convolutional Neural Network for Dynamic Contrast Enhanced MR Imaging of the Liver Tamada, Daiki Kromrey, Marie-Luise Ichikawa, Shintaro Onishi, Hiroshi Motosugi, Utaroh Magn Reson Med Sci Major Paper PURPOSE: To improve the quality of images obtained via dynamic contrast enhanced MRI (DCE-MRI), which contain motion artifacts and blurring using a deep learning approach. MATERIALS AND METHODS: A multi-channel convolutional neural network-based method is proposed for reducing the motion artifacts and blurring caused by respiratory motion in images obtained via DCE-MRI of the liver. The training datasets for the neural network included images with and without respiration-induced motion artifacts or blurring, and the distortions were generated by simulating the phase error in k-space. Patient studies were conducted using a multi-phase T(1)-weighted spoiled gradient echo sequence for the liver, which contained breath-hold failures occurring during data acquisition. The trained network was applied to the acquired images to analyze the filtering performance, and the intensities and contrast ratios before and after denoising were compared via Bland–Altman plots. RESULTS: The proposed network was found to be significantly reducing the magnitude of the artifacts and blurring induced by respiratory motion, and the contrast ratios of the images after processing via the network were consistent with those of the unprocessed images. CONCLUSION: A deep learning-based method for removing motion artifacts in images obtained via DCE-MRI of the liver was demonstrated and validated. Japanese Society for Magnetic Resonance in Medicine 2019-04-26 /pmc/articles/PMC7067907/ /pubmed/31061259 http://dx.doi.org/10.2463/mrms.mp.2018-0156 Text en © 2019 Japanese Society for Magnetic Resonance in Medicine This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/
spellingShingle Major Paper
Tamada, Daiki
Kromrey, Marie-Luise
Ichikawa, Shintaro
Onishi, Hiroshi
Motosugi, Utaroh
Motion Artifact Reduction Using a Convolutional Neural Network for Dynamic Contrast Enhanced MR Imaging of the Liver
title Motion Artifact Reduction Using a Convolutional Neural Network for Dynamic Contrast Enhanced MR Imaging of the Liver
title_full Motion Artifact Reduction Using a Convolutional Neural Network for Dynamic Contrast Enhanced MR Imaging of the Liver
title_fullStr Motion Artifact Reduction Using a Convolutional Neural Network for Dynamic Contrast Enhanced MR Imaging of the Liver
title_full_unstemmed Motion Artifact Reduction Using a Convolutional Neural Network for Dynamic Contrast Enhanced MR Imaging of the Liver
title_short Motion Artifact Reduction Using a Convolutional Neural Network for Dynamic Contrast Enhanced MR Imaging of the Liver
title_sort motion artifact reduction using a convolutional neural network for dynamic contrast enhanced mr imaging of the liver
topic Major Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7067907/
https://www.ncbi.nlm.nih.gov/pubmed/31061259
http://dx.doi.org/10.2463/mrms.mp.2018-0156
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