Test-retest repeatability of deep learning-based attenuation correction for chest FDG PET/MRI using Zero-TE MRI and unpaired PET/CT data

被引：0

作者：

Nogami, Munenobu ^{[1
,2
]}

Matsuo, Hidetoshi ^{[3
]}

Nishio, Mizuho ^{[3
]}

Zeng, Feibi ^{[1
]}

Tachibana, Miho ^{[3
]}

Inukai, Junko ^{[3
]}

Kurimoto, Takako ^{[4
]}

Kubo, Kazuhiro

Okazawa, Hidehiko ^{[2
]}

Murakami, Takamichi ^{[3
]}

机构：

[1] Kobe Univ Hosp, Kobe, Hyogo, Japan

[2] Univ Fukui, Fukui, Japan

[3] Kobe Univ, Grad Sch Med, Kobe, Hyogo, Japan

[4] GE Healthcare, Hino, Tokyo, Japan

来源：

JOURNAL OF NUCLEAR MEDICINE | 2024年 / 65卷

关键词：

D O I：

暂无

中图分类号：

R8 [特种医学]; R445 [影像诊断学];

学科分类号：

1002 ; 100207 ; 1009 ;

摘要：

241312

引用

页数：3

共 50 条

[1] External validation of deep learning-based attenuation correction for chest FDG PET/MRI using Zero-TE MRI and unpaired PET/CT data
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[2] PET attenuation correction of chest FDG PET/MRI: Deep learning-based denoising and pseudo-CT generation using fast zero-TE MRI and unpaired PET/CT data
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[5] 2.5-dimensional deep learning in Zero-TE MRI-based attenuation correction for chest FDG PET/MRI using unpaired PET/CT data: Assessment of quantitative values in pseudoCT generation and PET reconstruction
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