{"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}
引用次数: 0
Abstract
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.
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。
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.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
