Deep Learning-based Noise Reduction for Fast Volume Diffusion Tensor Imaging: Assessing the Noise Reduction Effect and Reliability of Diffusion Metrics

To assess the feasibility of a denoising approach with deep learning-based reconstruction (dDLR) for fast volume simultaneous multi-slice diffusion tensor imaging of the brain, noise reduction effects and the reliability of diffusion metrics were evaluated with 20 patients. Image noise was significa...

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Autores principales: Sagawa, Hajime, Fushimi, Yasutaka, Nakajima, Satoshi, Fujimoto, Koji, Miyake, Kanae Kawai, Numamoto, Hitomi, Koizumi, Koji, Nambu, Masahito, Kataoka, Hiroharu, Nakamoto, Yuji, Saga, Tsuneo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Japanese Society for Magnetic Resonance in Medicine 2020
Materias:
Technical Note
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8922344/
https://www.ncbi.nlm.nih.gov/pubmed/32963184
http://dx.doi.org/10.2463/mrms.tn.2020-0061
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author Sagawa, Hajime
Fushimi, Yasutaka
Nakajima, Satoshi
Fujimoto, Koji
Miyake, Kanae Kawai
Numamoto, Hitomi
Koizumi, Koji
Nambu, Masahito
Kataoka, Hiroharu
Nakamoto, Yuji
Saga, Tsuneo
author_facet Sagawa, Hajime
Fushimi, Yasutaka
Nakajima, Satoshi
Fujimoto, Koji
Miyake, Kanae Kawai
Numamoto, Hitomi
Koizumi, Koji
Nambu, Masahito
Kataoka, Hiroharu
Nakamoto, Yuji
Saga, Tsuneo
author_sort Sagawa, Hajime
collection PubMed
description To assess the feasibility of a denoising approach with deep learning-based reconstruction (dDLR) for fast volume simultaneous multi-slice diffusion tensor imaging of the brain, noise reduction effects and the reliability of diffusion metrics were evaluated with 20 patients. Image noise was significantly decreased with dDLR. Although fractional anisotropy (FA) of deep gray matter was overestimated when the number of image acquisitions was one (NAQ1), FA in NAQ1 with dDLR became closer to that in NAQ5.
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spelling pubmed-89223442022-03-28 Deep Learning-based Noise Reduction for Fast Volume Diffusion Tensor Imaging: Assessing the Noise Reduction Effect and Reliability of Diffusion Metrics Sagawa, Hajime Fushimi, Yasutaka Nakajima, Satoshi Fujimoto, Koji Miyake, Kanae Kawai Numamoto, Hitomi Koizumi, Koji Nambu, Masahito Kataoka, Hiroharu Nakamoto, Yuji Saga, Tsuneo Magn Reson Med Sci Technical Note To assess the feasibility of a denoising approach with deep learning-based reconstruction (dDLR) for fast volume simultaneous multi-slice diffusion tensor imaging of the brain, noise reduction effects and the reliability of diffusion metrics were evaluated with 20 patients. Image noise was significantly decreased with dDLR. Although fractional anisotropy (FA) of deep gray matter was overestimated when the number of image acquisitions was one (NAQ1), FA in NAQ1 with dDLR became closer to that in NAQ5. Japanese Society for Magnetic Resonance in Medicine 2020-09-18 /pmc/articles/PMC8922344/ /pubmed/32963184 http://dx.doi.org/10.2463/mrms.tn.2020-0061 Text en © 2020 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 Technical Note
Sagawa, Hajime
Fushimi, Yasutaka
Nakajima, Satoshi
Fujimoto, Koji
Miyake, Kanae Kawai
Numamoto, Hitomi
Koizumi, Koji
Nambu, Masahito
Kataoka, Hiroharu
Nakamoto, Yuji
Saga, Tsuneo
Deep Learning-based Noise Reduction for Fast Volume Diffusion Tensor Imaging: Assessing the Noise Reduction Effect and Reliability of Diffusion Metrics
title Deep Learning-based Noise Reduction for Fast Volume Diffusion Tensor Imaging: Assessing the Noise Reduction Effect and Reliability of Diffusion Metrics
title_full Deep Learning-based Noise Reduction for Fast Volume Diffusion Tensor Imaging: Assessing the Noise Reduction Effect and Reliability of Diffusion Metrics
title_fullStr Deep Learning-based Noise Reduction for Fast Volume Diffusion Tensor Imaging: Assessing the Noise Reduction Effect and Reliability of Diffusion Metrics
title_full_unstemmed Deep Learning-based Noise Reduction for Fast Volume Diffusion Tensor Imaging: Assessing the Noise Reduction Effect and Reliability of Diffusion Metrics
title_short Deep Learning-based Noise Reduction for Fast Volume Diffusion Tensor Imaging: Assessing the Noise Reduction Effect and Reliability of Diffusion Metrics
title_sort deep learning-based noise reduction for fast volume diffusion tensor imaging: assessing the noise reduction effect and reliability of diffusion metrics
topic Technical Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8922344/
https://www.ncbi.nlm.nih.gov/pubmed/32963184
http://dx.doi.org/10.2463/mrms.tn.2020-0061
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