Image Quality and Lesion Detection of Multiplanar Reconstruction Images Using Deep Learning: Comparison with Hybrid Iterative Reconstruction.

IF 0.9 4区 医学 Q4 MEDICINE, RESEARCH & EXPERIMENTAL Yonago acta medica Pub Date : 2024-04-22 eCollection Date: 2024-05-01 DOI:10.33160/yam.2024.05.001
Hiroto Yunaga, Hidenao Miyoshi, Ryoya Ochiai, Takuro Gonda, Toshio Sakoh, Hisashi Noma, Shinya Fujii
{"title":"Image Quality and Lesion Detection of Multiplanar Reconstruction Images Using Deep Learning: Comparison with Hybrid Iterative Reconstruction.","authors":"Hiroto Yunaga, Hidenao Miyoshi, Ryoya Ochiai, Takuro Gonda, Toshio Sakoh, Hisashi Noma, Shinya Fujii","doi":"10.33160/yam.2024.05.001","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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\".</p><p><strong>Methods: </strong>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 <i>t</i>-test and Wilcoxon's signed-rank test. The Bonferroni correction was applied to the P value, and significance was assumed only for values of <i>P</i> < 0.016.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":23795,"journal":{"name":"Yonago acta medica","volume":"67 2","pages":"100-107"},"PeriodicalIF":0.9000,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11128077/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Yonago acta medica","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.33160/yam.2024.05.001","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/5/1 0:00:00","PubModel":"eCollection","JCR":"Q4","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0

Abstract

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.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
使用深度学习的多平面重建图像的图像质量和病灶检测:与混合迭代重建的比较
背景我们评估并比较了使用 "自适应统计迭代重建-V"(ASiR-V)或深度学习重建 "TrueFidelity "的胸部计算机断层扫描图像中正常和病理结构的图像质量以及图像噪声:方法: 对 40 名疑似肺部疾病的连续患者进行了评估。在以下三种条件下对 1.25 毫米轴向图像和 2.0 毫米冠状多平面图像进行了重建:(i) ASiR-V,60% ASiR-V 的肺部内核;(ii) TF-M,标准内核,中等强度 TrueFidelity 图像滤波器(肺部);(iii) TF-H,标准内核,高强度 TrueFidelity 图像滤波器(肺部)。两名放射科医生(阅读者)使用从 1(最佳)到 5(最差)的量表独立评估解剖结构的图像质量。此外,读者还对自己的图像偏好进行了排序。客观图像噪声使用肺实质中的圆形感兴趣区进行测量。采用 Wilcoxon 符号秩检验比较主观图像质量得分、正常和异常结构总分以及病变检测。客观图像质量采用学生配对 t 检验和 Wilcoxon 符号秩检验进行比较。对 P 值进行 Bonferroni 校正,仅当 P 值小于 0.016 时才假定显著性:两位读者对 TF-M 和 TF-H 图像在轴向图像中中央叶区可视化方面的评分均明显优于 ASiR-V 图像。读者 1 对 TF-M 和 TF-H 图像的偏好评分优于 ASiR-V 图像,读者 2 对 TF-H 图像的偏好评分明显优于 ASiR-V 和 TF-M 图像。TF-M 图像的客观图像噪声明显低于 ASiR-V 或 TF-H 图像:结论:在主观和客观评价方面,TrueFidelity 的图像质量(尤其是在中央叶区)均优于 ASiR-V。此外,TrueFidelity 对图像纹理的偏好也优于 ASiR-V。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Yonago acta medica
Yonago acta medica MEDICINE, RESEARCH & EXPERIMENTAL-
CiteScore
1.60
自引率
0.00%
发文量
36
审稿时长
>12 weeks
期刊介绍: Yonago Acta Medica (YAM) is an electronic journal specializing in medical sciences, published by Tottori University Medical Press, 86 Nishi-cho, Yonago 683-8503, Japan. The subject areas cover the following: molecular/cell biology; biochemistry; basic medicine; clinical medicine; veterinary medicine; clinical nutrition and food sciences; medical engineering; nursing sciences; laboratory medicine; clinical psychology; medical education. Basically, contributors are limited to members of Tottori University and Tottori University Hospital. Researchers outside the above-mentioned university community may also submit papers on the recommendation of a professor, an associate professor, or a junior associate professor at this university community. Articles are classified into four categories: review articles, original articles, patient reports, and short communications.
期刊最新文献
The Influence of Total Fat Mass and Skeletal Muscle Mass Index on the Occurrence of Perioperative Hypothermia in Patients Undergoing Open Gastrectomy. Effectiveness of Online Parent Training for Parents of Adolescents with Developmental Disabilities: A Retrospective Observational Study Comparing Clinical- and Community-Based Online Parent Training. Parents' Expectations of Parent Training programs for Parents of Children with Neurodevelopmental Disorders in Japan. Impact of Lymphocyte/Monocyte Ratio on Outcomes in Patients with Remnant Gastric Cancer After Gastrectomy. Parental Issues and Support Needs in Selective Mutism in Japan: A Quantitative Content Analysis.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1