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MR Imaging of Endolymphatic Hydrops: Utility of iHYDROPS-Mi2 Combined with Deep Learning Reconstruction Denoising
PURPOSE: MRI of endolymphatic hydrops (EH) 4 h after intravenous administration of a single dose of gadolinium-based contrast agent is used for clinical examination in some institutions; however, further improvement in image quality would be valuable for wider clinical utility. Denoising using deep...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Japanese Society for Magnetic Resonance in Medicine
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8424026/ https://www.ncbi.nlm.nih.gov/pubmed/32830173 http://dx.doi.org/10.2463/mrms.mp.2020-0082 |
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author | Naganawa, Shinji Nakamichi, Rei Ichikawa, Kazushige Kawamura, Mariko Kawai, Hisashi Yoshida, Tadao Sone, Michihiko |
author_facet | Naganawa, Shinji Nakamichi, Rei Ichikawa, Kazushige Kawamura, Mariko Kawai, Hisashi Yoshida, Tadao Sone, Michihiko |
author_sort | Naganawa, Shinji |
collection | PubMed |
description | PURPOSE: MRI of endolymphatic hydrops (EH) 4 h after intravenous administration of a single dose of gadolinium-based contrast agent is used for clinical examination in some institutions; however, further improvement in image quality would be valuable for wider clinical utility. Denoising using deep learning reconstruction (Advanced Intelligent Clear-IQ Engine [AiCE]) has been reported for CT and MR. The purpose of this study was to compare the contrast-to-noise ratio of endolymph to perilymph (CNR(EP)) between the improved hybrid of reversed image of the positive endolymph signal and the native image of the perilymph signal multiplied with the heavily T(2)-weighted MR cisternography (iHYDROPS-Mi2) images, which used AiCE for the three source images (i.e. positive endolymph image [PEI], positive perilymph image [PPI], MR cisternography [MRC]) to those that did not use AiCE. We also examined if there was a difference between iHYDROPS-Mi2 images with and without AiCE for degree of visual grading of EH and in endolymphatic area [EL] ratios. METHODS: Nine patients with suspicion of EH were imaged on a 3T MR scanner. iHYDROPS images were generated by subtraction of PEI images from PPI images. iHYDROPS-Mi2 images were then generated by multiplying MRC with iHYDROPS images. The CNR(EP) and EL ratio were measured on the iHYDROPS-Mi2 images. Degree of radiologist visual grading for EH was evaluated. RESULTS: Mean CNR(EP) ± standard deviation was 1681.8 ± 845.2 without AiCE and 7738.6 ± 5149.2 with AiCE (P = 0.00002). There was no significant difference in EL ratio for images with and without AiCE. Radiologist grading for EH agreed completely between the 2 image types in both the cochlea and vestibule. CONCLUSION: The CNR(EP) of iHYDROPS-Mi2 images with AiCE had more than a fourfold increase compared with that without AiCE. Use of AiCE did not adversely affect radiologist grading of EH. |
format | Online Article Text |
id | pubmed-8424026 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Japanese Society for Magnetic Resonance in Medicine |
record_format | MEDLINE/PubMed |
spelling | pubmed-84240262021-09-24 MR Imaging of Endolymphatic Hydrops: Utility of iHYDROPS-Mi2 Combined with Deep Learning Reconstruction Denoising Naganawa, Shinji Nakamichi, Rei Ichikawa, Kazushige Kawamura, Mariko Kawai, Hisashi Yoshida, Tadao Sone, Michihiko Magn Reson Med Sci Major Paper PURPOSE: MRI of endolymphatic hydrops (EH) 4 h after intravenous administration of a single dose of gadolinium-based contrast agent is used for clinical examination in some institutions; however, further improvement in image quality would be valuable for wider clinical utility. Denoising using deep learning reconstruction (Advanced Intelligent Clear-IQ Engine [AiCE]) has been reported for CT and MR. The purpose of this study was to compare the contrast-to-noise ratio of endolymph to perilymph (CNR(EP)) between the improved hybrid of reversed image of the positive endolymph signal and the native image of the perilymph signal multiplied with the heavily T(2)-weighted MR cisternography (iHYDROPS-Mi2) images, which used AiCE for the three source images (i.e. positive endolymph image [PEI], positive perilymph image [PPI], MR cisternography [MRC]) to those that did not use AiCE. We also examined if there was a difference between iHYDROPS-Mi2 images with and without AiCE for degree of visual grading of EH and in endolymphatic area [EL] ratios. METHODS: Nine patients with suspicion of EH were imaged on a 3T MR scanner. iHYDROPS images were generated by subtraction of PEI images from PPI images. iHYDROPS-Mi2 images were then generated by multiplying MRC with iHYDROPS images. The CNR(EP) and EL ratio were measured on the iHYDROPS-Mi2 images. Degree of radiologist visual grading for EH was evaluated. RESULTS: Mean CNR(EP) ± standard deviation was 1681.8 ± 845.2 without AiCE and 7738.6 ± 5149.2 with AiCE (P = 0.00002). There was no significant difference in EL ratio for images with and without AiCE. Radiologist grading for EH agreed completely between the 2 image types in both the cochlea and vestibule. CONCLUSION: The CNR(EP) of iHYDROPS-Mi2 images with AiCE had more than a fourfold increase compared with that without AiCE. Use of AiCE did not adversely affect radiologist grading of EH. Japanese Society for Magnetic Resonance in Medicine 2020-08-21 /pmc/articles/PMC8424026/ /pubmed/32830173 http://dx.doi.org/10.2463/mrms.mp.2020-0082 Text en © 2021 Japanese Society for Magnetic Resonance in Medicine https://creativecommons.org/licenses/by-nc-nd/4.0/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/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) |
spellingShingle | Major Paper Naganawa, Shinji Nakamichi, Rei Ichikawa, Kazushige Kawamura, Mariko Kawai, Hisashi Yoshida, Tadao Sone, Michihiko MR Imaging of Endolymphatic Hydrops: Utility of iHYDROPS-Mi2 Combined with Deep Learning Reconstruction Denoising |
title | MR Imaging of Endolymphatic Hydrops: Utility of iHYDROPS-Mi2 Combined with Deep Learning Reconstruction Denoising |
title_full | MR Imaging of Endolymphatic Hydrops: Utility of iHYDROPS-Mi2 Combined with Deep Learning Reconstruction Denoising |
title_fullStr | MR Imaging of Endolymphatic Hydrops: Utility of iHYDROPS-Mi2 Combined with Deep Learning Reconstruction Denoising |
title_full_unstemmed | MR Imaging of Endolymphatic Hydrops: Utility of iHYDROPS-Mi2 Combined with Deep Learning Reconstruction Denoising |
title_short | MR Imaging of Endolymphatic Hydrops: Utility of iHYDROPS-Mi2 Combined with Deep Learning Reconstruction Denoising |
title_sort | mr imaging of endolymphatic hydrops: utility of ihydrops-mi2 combined with deep learning reconstruction denoising |
topic | Major Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8424026/ https://www.ncbi.nlm.nih.gov/pubmed/32830173 http://dx.doi.org/10.2463/mrms.mp.2020-0082 |
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