| Item type |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2024-08-28 |
| タイトル |
|
|
タイトル |
Image Quality and Lesion Detection of Multiplanar Reconstruction Images Using Deep Learning: Comparison with Hybrid Iterative Reconstruction |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
deep learning |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
diagnostic imaging |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
image processing |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
computer-assisted |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
lung |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
multidetector computed tomography |
| 資源タイプ |
|
|
資源タイプ |
journal article |
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 著者 |
Yunaga,Hiroto
Miyoshi,Hidenao
Ochiai,Ryoya
Gonda,Takuro
Sakoh,Toshio
Noma,Hisashi
藤井,進也
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| 著者所属（英） |
|
|
言語 |
en |
|
値 |
Division of Radiology, Department of Multidisciplinary Internal Medicine, School of Medicine, Faculty of Medicine, Tottori University |
| 著者所属（英） |
|
|
言語 |
en |
|
値 |
Division of Radiology, Department of Multidisciplinary Internal Medicine, School of Medicine, Faculty of Medicine, Tottori University |
| 著者所属（英） |
|
|
言語 |
en |
|
値 |
Division of Radiology, Department of Multidisciplinary Internal Medicine, School of Medicine, Faculty of Medicine, Tottori University |
| 著者所属（英） |
|
|
言語 |
en |
|
値 |
Division of Radiology, Department of Multidisciplinary Internal Medicine, School of Medicine, Faculty of Medicine, Tottori University |
| 著者所属（英） |
|
|
言語 |
en |
|
値 |
Division of Clinical Radiology, School of Medicine, Faculty of Medicine, Tottori University |
| 著者所属（英） |
|
|
言語 |
en |
|
値 |
Department of Data Science, The Institute of Statistical Mathematics |
| 著者所属（英） |
|
|
言語 |
en |
|
値 |
Division of Radiology, Department of Multidisciplinary Internal Medicine, School of Medicine, Faculty of Medicine, Tottori University |
| 抄録 |
|
|
内容記述タイプ |
Other |
|
内容記述 |
Background: We assessed and compared the image quality of normal and pathologic structures as well as the image noise in chest computed tomography images using “adaptive statistical iterative reconstruction-V” (ASiR-V) or deep learning reconstruction “TrueFidelity”. Methods: Forty consecutive patients with suspected lung disease were evaluated. The 1.25-mm axial images and 2.0-mm coronal multiplanar images were reconstructed under the following three conditions: (i) ASiR-V, lung kernel with 60% of ASiR-V; (ii) TF-M, standard kernel, image filter (Lung) with TrueFidelity at medium strength; and (iii) TF-H, standard kernel, image filter (Lung) with TrueFidelity at high strength. Two radiologists (readers) independently evaluated the image quality of anatomic structures using a scale ranging from 1 (best) to 5 (worst). In addition, readers ranked their image preference. Objective image noise was measured using a circular region of interest in the lung parenchyma. Subjective image quality scores, total scores for normal and abnormal structures, and lesion detection were compared using Wilcoxon’s signed-rank test. Objective image quality was compared using Student’s paired t-test and Wilcoxon’s signed-rank test. The Bonferroni correction was applied to the P value, and significance was assumed only for values of P < 0.016. Results: Both readers rated TF-M and TF-H images significantly better than ASiR-V images in terms of visualization of the centrilobular region in axial images. The preference score of TF-M and TF-H images for reader 1 were better than that of ASiR-V images, and the preference score of TF-H images for reader 2 were significantly better than that of ASiR-V and TF-M images. TF-M images showed significantly lower objective image noise than ASiR-V or TF-H images. Conclusion: TrueFidelity showed better image quality, especially in the centrilobular region, than ASiR-V in subjective and objective evaluations. In addition, the image texture preference for TrueFidelity was better than that for ASiR-V. |
|
言語 |
en |
| 書誌情報 |
en : Yonago Acta Medica
巻 67,
号 2,
p. 100-107,
ページ数 8,
発行日 2024-05-28
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| 出版者 |
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|
出版者 |
Tottori University Medical Press |
|
言語 |
en |
| ISSN |
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収録物識別子タイプ |
PISSN |
|
収録物識別子 |
05135710 |
| ISSN |
|
|
収録物識別子タイプ |
EISSN |
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収録物識別子 |
13468049 |
| 書誌レコードID |
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収録物識別子タイプ |
NCID |
|
収録物識別子 |
AA00892882 |
| 権利 |
|
|
言語 |
en |
|
権利情報 |
(C)2024 Tottori University Medical Press |
| 情報源 |
|
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|
関連名称 |
Yonago Acta Medica. 2024, 67(2), 100-107. |
| 関連サイト |
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|
識別子タイプ |
URI |
|
|
関連識別子 |
https://www.lib.tottori-u.ac.jp/yam/yam/yam67-2/67-2contents.html |
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|
関連名称 |
https://www.lib.tottori-u.ac.jp/yam/yam/yam67-2/67-2contents.html |
| 関連サイト |
|
|
関連タイプ |
isVersionOf |
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|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.33160/yam.2024.05.001 |
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|
関連名称 |
https://doi.org/10.33160/yam.2024.05.001 |
| 著者版フラグ |
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|
出版タイプ |
VoR |
yam66(4)_100.pdf (2.4 MB)
