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...
Autores principales: | , , , , |
---|---|
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 |
_version_ | 1783505482402496512 |
---|---|
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. |
format | Online Article Text |
id | pubmed-7067907 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Japanese Society for Magnetic Resonance in Medicine |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT tamadadaiki motionartifactreductionusingaconvolutionalneuralnetworkfordynamiccontrastenhancedmrimagingoftheliver AT kromreymarieluise motionartifactreductionusingaconvolutionalneuralnetworkfordynamiccontrastenhancedmrimagingoftheliver AT ichikawashintaro motionartifactreductionusingaconvolutionalneuralnetworkfordynamiccontrastenhancedmrimagingoftheliver AT onishihiroshi motionartifactreductionusingaconvolutionalneuralnetworkfordynamiccontrastenhancedmrimagingoftheliver AT motosugiutaroh motionartifactreductionusingaconvolutionalneuralnetworkfordynamiccontrastenhancedmrimagingoftheliver |