在前列腺 T2 加权图像中,验证通过深度学习重构 1.5 T MRI 提高图像质量的效果。

IF 1.7 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Radiological Physics and Technology Pub Date : 2024-09-01 Epub Date: 2024-06-08 DOI:10.1007/s12194-024-00819-5
Yoshiomi Sato, Kiyoshi Ohkuma
{"title":"在前列腺 T2 加权图像中,验证通过深度学习重构 1.5 T MRI 提高图像质量的效果。","authors":"Yoshiomi Sato, Kiyoshi Ohkuma","doi":"10.1007/s12194-024-00819-5","DOIUrl":null,"url":null,"abstract":"<p><p>This study aimed to evaluate whether the image quality of 1.5 T magnetic resonance imaging (MRI) of the prostate is equal to or higher than that of 3 T MRI by applying deep learning reconstruction (DLR). To objectively analyze the images from the 13 healthy volunteers, we measured the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the images obtained by the 1.5 T scanner with and without DLR, as well as for images obtained by the 3 T scanner. In the subjective, T2W images of the prostate were visually evaluated by two board-certified radiologists. The SNRs and CNRs in 1.5 T images with DLR were higher than that in 3 T images. Subjective image scores were better for 1.5 T images with DLR than 3 T images. The use of the DLR technique in 1.5 T MRI substantially improved the SNR and image quality of T2W images of the prostate gland, as compared to 3 T MRI.</p>","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"756-764"},"PeriodicalIF":1.7000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Verification of image quality improvement by deep learning reconstruction to 1.5 T MRI in T2-weighted images of the prostate gland.\",\"authors\":\"Yoshiomi Sato, Kiyoshi Ohkuma\",\"doi\":\"10.1007/s12194-024-00819-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This study aimed to evaluate whether the image quality of 1.5 T magnetic resonance imaging (MRI) of the prostate is equal to or higher than that of 3 T MRI by applying deep learning reconstruction (DLR). To objectively analyze the images from the 13 healthy volunteers, we measured the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the images obtained by the 1.5 T scanner with and without DLR, as well as for images obtained by the 3 T scanner. In the subjective, T2W images of the prostate were visually evaluated by two board-certified radiologists. The SNRs and CNRs in 1.5 T images with DLR were higher than that in 3 T images. Subjective image scores were better for 1.5 T images with DLR than 3 T images. The use of the DLR technique in 1.5 T MRI substantially improved the SNR and image quality of T2W images of the prostate gland, as compared to 3 T MRI.</p>\",\"PeriodicalId\":46252,\"journal\":{\"name\":\"Radiological Physics and Technology\",\"volume\":\" \",\"pages\":\"756-764\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2024-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Radiological Physics and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s12194-024-00819-5\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/6/8 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Radiological Physics and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s12194-024-00819-5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/6/8 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0

摘要

本研究旨在通过应用深度学习重建(DLR)评估前列腺 1.5 T 磁共振成像(MRI)的图像质量是否等于或高于 3 T MRI。为了客观分析 13 名健康志愿者的图像,我们测量了使用 1.5 T 扫描仪和不使用 DLR 所获得图像的信噪比(SNR)和对比度-噪声比(CNR),以及使用 3 T 扫描仪所获得图像的信噪比(SNR)和对比度-噪声比(CNR)。在主观评估中,前列腺的 T2W 图像由两名经委员会认证的放射科医生进行目测评估。使用 DLR 的 1.5 T 图像的 SNR 和 CNR 均高于 3 T 图像。使用 DLR 的 1.5 T 图像的主观图像评分优于 3 T 图像。与 3 T 磁共振成像相比,在 1.5 T 磁共振成像中使用 DLR 技术大大提高了前列腺 T2W 图像的信噪比和图像质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Verification of image quality improvement by deep learning reconstruction to 1.5 T MRI in T2-weighted images of the prostate gland.

This study aimed to evaluate whether the image quality of 1.5 T magnetic resonance imaging (MRI) of the prostate is equal to or higher than that of 3 T MRI by applying deep learning reconstruction (DLR). To objectively analyze the images from the 13 healthy volunteers, we measured the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the images obtained by the 1.5 T scanner with and without DLR, as well as for images obtained by the 3 T scanner. In the subjective, T2W images of the prostate were visually evaluated by two board-certified radiologists. The SNRs and CNRs in 1.5 T images with DLR were higher than that in 3 T images. Subjective image scores were better for 1.5 T images with DLR than 3 T images. The use of the DLR technique in 1.5 T MRI substantially improved the SNR and image quality of T2W images of the prostate gland, as compared to 3 T MRI.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Radiological Physics and Technology
Radiological Physics and Technology RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
3.00
自引率
12.50%
发文量
40
期刊介绍: The purpose of the journal Radiological Physics and Technology is to provide a forum for sharing new knowledge related to research and development in radiological science and technology, including medical physics and radiological technology in diagnostic radiology, nuclear medicine, and radiation therapy among many other radiological disciplines, as well as to contribute to progress and improvement in medical practice and patient health care.
期刊最新文献
Acknowledgment. Evaluation of calculation accuracy and computation time in a commercial treatment planning system for accelerator-based boron neutron capture therapy. Development of deep learning-based novel auto-segmentation for the prostatic urethra on planning CT images for prostate cancer radiotherapy. Effect of deep learning reconstruction on the assessment of pancreatic cystic lesions using computed tomography. Assessment of accuracy and repeatability of quantitative parameter mapping in MRI.
×
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