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 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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:20 |
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| Enthalten in: |
Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine - 20(2021), 4 vom: 01. Dez., Seite 450-456 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Sagawa, Hajime [VerfasserIn] |
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| Links: |
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| Themen: |
Denoising approach with deep learning-based reconstruction |
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| Anmerkungen: |
Date Completed 03.12.2021 Date Revised 29.03.2022 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.2463/mrms.tn.2020-0061 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM31534363X |
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| 245 | 1 | 0 | |a Deep Learning-based Noise Reduction for Fast Volume Diffusion Tensor Imaging |b Assessing the Noise Reduction Effect and Reliability of Diffusion Metrics |
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| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a denoising approach with deep learning-based reconstruction | |
| 650 | 4 | |a diffusion tensor imaging | |
| 650 | 4 | |a diffusion tensor tractography | |
| 650 | 4 | |a number of image acquisition | |
| 700 | 1 | |a Fushimi, Yasutaka |e verfasserin |4 aut | |
| 700 | 1 | |a Nakajima, Satoshi |e verfasserin |4 aut | |
| 700 | 1 | |a Fujimoto, Koji |e verfasserin |4 aut | |
| 700 | 1 | |a Miyake, Kanae Kawai |e verfasserin |4 aut | |
| 700 | 1 | |a Numamoto, Hitomi |e verfasserin |4 aut | |
| 700 | 1 | |a Koizumi, Koji |e verfasserin |4 aut | |
| 700 | 1 | |a Nambu, Masahito |e verfasserin |4 aut | |
| 700 | 1 | |a Kataoka, Hiroharu |e verfasserin |4 aut | |
| 700 | 1 | |a Nakamoto, Yuji |e verfasserin |4 aut | |
| 700 | 1 | |a Saga, Tsuneo |e verfasserin |4 aut | |
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