MR Imaging of Endolymphatic Hydrops: Utility of iHYDROPS-Mi2 Combined with Deep Learning Reconstruction Denoising.

deep learning reconstruction endolymphatic hydrops gadolinium inner ear magnetic resonance imaging

Journal

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
ISSN: 1880-2206
Titre abrégé: Magn Reson Med Sci
Pays: Japan
ID NLM: 101153368

Informations de publication

Date de publication:
01 Sep 2021
Historique:
pubmed: 25 8 2020
medline: 26 11 2021
entrez: 25 8 2020
Statut: ppublish

Résumé

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 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 Mean CNR The CNR

Identifiants

pubmed: 32830173
doi: 10.2463/mrms.mp.2020-0082
pmc: PMC8424026
doi:

Substances chimiques

Contrast Media 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

272-279

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Auteurs

Shinji Naganawa (S)

Department of Radiology, Nagoya University Graduate School of Medicine.

Rei Nakamichi (R)

Department of Radiology, Nagoya University Graduate School of Medicine.

Kazushige Ichikawa (K)

Department of Radiology, Nagoya University Graduate School of Medicine.

Mariko Kawamura (M)

Department of Radiology, Nagoya University Graduate School of Medicine.

Hisashi Kawai (H)

Department of Radiology, Nagoya University Graduate School of Medicine.

Tadao Yoshida (T)

Department of Otorhinolaryngology, Nagoya University Graduate School of Medicine.

Michihiko Sone (M)

Department of Otorhinolaryngology, Nagoya University Graduate School of Medicine.

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Classifications MeSH