• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

深度学习加速 1.5T 和 3T 单次屏气腹部 HASTE 的全面临床评估。

Comprehensive Clinical Evaluation of a Deep Learning-Accelerated, Single-Breath-Hold Abdominal HASTE at 1.5 T and 3 T.

机构信息

Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany.

MR Applications Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany.

出版信息

Acad Radiol. 2023 Jan;30(1):93-102. doi: 10.1016/j.acra.2022.03.018. Epub 2022 Apr 22.

DOI:10.1016/j.acra.2022.03.018
PMID:35469719
Abstract

To evaluate the clinical performance of a deep learning-accelerated single-breath-hold half-Fourier acquisition single-shot turbo spin echo (HASTE)-sequence for T2-weighted fat-suppressed MRI of the abdomen at 1.5 T and 3 T in comparison to standard T2-weighted fat-suppressed multi-shot turbo spin echo-sequence. A total of 320 patients who underwent a clinically indicated liver MRI at 1.5 T and 3 T between August 2020 and February 2021 were enrolled in this single-center, retrospective study. HASTE and standard sequences were assessed regarding overall and organ-based image quality, noise, contrast, sharpness, artifacts, diagnostic confidence, as well as lesion detectability using a Likert scale ranging from 1 to 4 (4 = best). The number of visible lesions of each organ was counted and the largest diameter of the major lesion was measured. HASTE showed excellent image quality (median 4, interquartile range 3-4), although BLADE (median 4, interquartile range 4-4) was rated significantly higher for overall and organ-based image quality of the adrenal gland (P < .001), contrast (P < 0.001), sharpness (P < 0.001), artifacts (P < 0.001), as well as diagnostic confidence (P < .001). No significant differences were found concerning noise (P = 0.886), organ-based image quality of the liver, pancreas, spleen, and kidneys (P = 0.120-0.366), number and measured diameter of the detected lesions (ICC = 0.972-1.0). Reduction of the aquisition time (TA) was at least 89% for 1.5 T images and 86% for 3 T images. HASTE provided excellent image quality, good diagnostic confidence and lesion detection compared to a standard T2-sequences, allowing an eminent reduction of the acquisition time.

摘要

为了评估深度学习加速的单次屏气半傅里叶采集单次激发涡轮自旋回波(HASTE)序列在 1.5T 和 3T 下用于腹部 T2 加权脂肪抑制 MRI 的临床性能,并与标准 T2 加权脂肪抑制多激发涡轮自旋回波序列进行比较。这项单中心回顾性研究共纳入了 2020 年 8 月至 2021 年 2 月期间在 1.5T 和 3T 进行临床指征性肝脏 MRI 检查的 320 名患者。评估了 HASTE 和标准序列的整体和器官基础的图像质量、噪声、对比度、锐利度、伪影、诊断信心,以及使用 1 到 4 分(4 分表示最佳)的李克特量表评估的病变可检测性。对每个器官的可见病变数量进行计数,并测量主要病变的最大直径。HASTE 显示出出色的图像质量(中位数 4,四分位距 3-4),尽管 BLADE(中位数 4,四分位距 4-4)在肾上腺的整体和器官基础的图像质量(P<0.001)、对比度(P<0.001)、锐利度(P<0.001)、伪影(P<0.001)以及诊断信心(P<0.001)方面的评分更高。在噪声(P=0.886)、肝脏、胰腺、脾脏和肾脏的器官基础图像质量(P=0.120-0.366)、检测到的病变数量和测量直径(ICC=0.972-1.0)方面无显著差异。1.5T 图像的采集时间(TA)减少至少 89%,3T 图像的采集时间减少至少 86%。与标准 T2 序列相比,HASTE 提供了出色的图像质量、良好的诊断信心和病变检测,允许显著减少采集时间。

相似文献

1
Comprehensive Clinical Evaluation of a Deep Learning-Accelerated, Single-Breath-Hold Abdominal HASTE at 1.5 T and 3 T.深度学习加速 1.5T 和 3T 单次屏气腹部 HASTE 的全面临床评估。
Acad Radiol. 2023 Jan;30(1):93-102. doi: 10.1016/j.acra.2022.03.018. Epub 2022 Apr 22.
2
Diagnostic Confidence and Feasibility of a Deep Learning Accelerated HASTE Sequence of the Abdomen in a Single Breath-Hold.深度学习加速单次屏气下腹部 HASTE 序列的诊断信心和可行性。
Invest Radiol. 2021 May 1;56(5):313-319. doi: 10.1097/RLI.0000000000000743.
3
Development and Evaluation of Deep Learning-Accelerated Single-Breath-Hold Abdominal HASTE at 3 T Using Variable Refocusing Flip Angles.深度学习加速的 3T 单次屏气腹部 HASTE 序列:可变重聚焦翻转角的研发与评估。
Invest Radiol. 2021 Oct 1;56(10):645-652. doi: 10.1097/RLI.0000000000000785.
4
Fast T2-weighted liver MRI: Image quality and solid focal lesions conspicuity using a deep learning accelerated single breath-hold HASTE fat-suppressed sequence.快速 T2 加权肝脏 MRI:使用深度学习加速单次屏气 HASTE 脂肪抑制序列的图像质量和实性局灶性病变显示度。
Diagn Interv Imaging. 2022 Oct;103(10):479-485. doi: 10.1016/j.diii.2022.05.001. Epub 2022 May 18.
5
Deep learning HASTE sequence compared with T2-weighted BLADE sequence for liver MRI at 3 Tesla: a qualitative and quantitative prospective study.深度学习 HASTE 序列与 T2 加权 BLADE 序列在 3.0T 肝脏 MRI 中的比较:一项定性和定量前瞻性研究。
Eur Radiol. 2023 Oct;33(10):6817-6827. doi: 10.1007/s00330-023-09693-y. Epub 2023 May 16.
6
Combined Deep Learning-based Super-Resolution and Partial Fourier Reconstruction for Gradient Echo Sequences in Abdominal MRI at 3 Tesla: Shortening Breath-Hold Time and Improving Image Sharpness and Lesion Conspicuity.基于深度学习的超分辨率与部分傅里叶重建相结合用于3特斯拉腹部磁共振成像梯度回波序列:缩短屏气时间并提高图像清晰度和病变可见性
Acad Radiol. 2023 May;30(5):863-872. doi: 10.1016/j.acra.2022.06.003. Epub 2022 Jul 6.
7
Accelerated single-shot T2-weighted fat-suppressed (FS) MRI of the liver with deep learning-based image reconstruction: qualitative and quantitative comparison of image quality with conventional T2-weighted FS sequence.基于深度学习的图像重建的肝脏加速单次激发 T2 加权脂肪抑制(FS)MRI:与常规 T2 加权 FS 序列的图像质量的定性和定量比较。
Eur Radiol. 2021 Nov;31(11):8447-8457. doi: 10.1007/s00330-021-08008-3. Epub 2021 May 7.
8
Usefulness of Breath-Hold Fat-Suppressed T2-Weighted Images With Deep Learning-Based Reconstruction of the Liver: Comparison to Conventional Free-Breathing Turbo Spin Echo.基于深度学习的肝脏屏气脂肪抑制 T2 加权成像的应用价值:与常规自由呼吸 Turbo 自旋回波的比较。
Invest Radiol. 2023 Jun 1;58(6):373-379. doi: 10.1097/RLI.0000000000000943. Epub 2022 Dec 26.
9
Clinical feasibility of deep learning-accelerated single-shot turbo spin echo sequence with enhanced denoising for pancreas MRI at 3 Tesla.在3特斯拉场强下,深度学习加速的具有增强去噪功能的单次激发快速自旋回波序列用于胰腺磁共振成像的临床可行性。
Eur J Radiol. 2024 Dec;181:111737. doi: 10.1016/j.ejrad.2024.111737. Epub 2024 Sep 15.
10
Accelerated T2-weighted MRI of the liver at 3 T using a single-shot technique with deep learning-based image reconstruction: impact on the image quality and lesion detection.3T 场强下基于深度学习的单次激发技术加速肝脏 T2 加权成像:对图像质量和病灶检出的影响。
Abdom Radiol (NY). 2023 Jan;48(1):282-290. doi: 10.1007/s00261-022-03687-y. Epub 2022 Sep 28.

引用本文的文献

1
Advances in IPMN imaging: deep learning-enhanced HASTE improves lesion assessment.胰腺导管内乳头状黏液性肿瘤(IPMN)成像的进展:深度学习增强的快速自旋回波(HASTE)改善病变评估。
Eur Radiol. 2025 Jul 21. doi: 10.1007/s00330-025-11857-x.
2
Ultrafast T2-weighted MR imaging of the urinary bladder using deep learning-accelerated HASTE at 3 Tesla.使用深度学习加速的快速自旋回波序列在3特斯拉场强下对膀胱进行超快T2加权磁共振成像。
BMC Med Imaging. 2025 Jul 15;25(1):284. doi: 10.1186/s12880-025-01810-1.
3
Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing Integration.
推进磁共振成像重建:深度学习与压缩感知集成的系统评价
ArXiv. 2025 Feb 1:arXiv:2501.14158v2.
4
Deep-Learning-Based Reconstruction of Single-Breath-Hold 3 mm HASTE Improves Abdominal Image Quality and Reduces Acquisition Time: A Quantitative Analysis.基于深度学习的单屏气 3 毫米快速自旋回波序列重建可改善腹部图像质量并缩短采集时间:一项定量分析。
Curr Oncol. 2025 Jan 3;32(1):30. doi: 10.3390/curroncol32010030.
5
Faster Acquisition and Improved Image Quality of T2-Weighted Dixon Breast MRI at 3T Using Deep Learning: A Prospective Study.使用深度学习在3T下实现T2加权 Dixon乳腺MRI的更快采集及图像质量改善:一项前瞻性研究
Korean J Radiol. 2025 Jan;26(1):29-42. doi: 10.3348/kjr.2023.1303.
6
Reducing energy consumption in musculoskeletal MRI using shorter scan protocols, optimized magnet cooling patterns, and deep learning sequences.使用更短的扫描协议、优化的磁体冷却模式和深度学习序列来降低肌肉骨骼MRI中的能量消耗。
Eur Radiol. 2025 Apr;35(4):1993-2004. doi: 10.1007/s00330-024-11056-0. Epub 2024 Sep 7.
7
Advanced MRI techniques in abdominal imaging.腹部影像学中的高级 MRI 技术。
Abdom Radiol (NY). 2024 Oct;49(10):3615-3636. doi: 10.1007/s00261-024-04369-7. Epub 2024 May 28.
8
Fast MRI Reconstruction Using Deep Learning-based Compressed Sensing: A Systematic Review.基于深度学习的压缩感知的快速磁共振成像重建:系统综述。
ArXiv. 2024 Apr 30:arXiv:2405.00241v1.
9
Bladder MRI with deep learning-based reconstruction: a prospective evaluation of muscle invasiveness using VI-RADS.基于深度学习的重建的膀胱 MRI:使用 VI-RADS 评估肌肉侵犯的前瞻性研究。
Abdom Radiol (NY). 2024 May;49(5):1615-1625. doi: 10.1007/s00261-024-04280-1. Epub 2024 Apr 23.
10
Application of a Deep Learning Algorithm for Combined Super-Resolution and Partial Fourier Reconstruction Including Time Reduction in T1-Weighted Precontrast and Postcontrast Gradient Echo Imaging of Abdominopelvic MR Imaging.一种深度学习算法在腹部盆腔磁共振成像T1加权对比前和对比后梯度回波成像中的应用,该算法用于联合超分辨率和部分傅里叶重建,包括减少时间。
Diagnostics (Basel). 2022 Sep 29;12(10):2370. doi: 10.3390/diagnostics12102370.