• 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

压缩感知和深度学习重建在女性盆腔 MRI 去噪中的应用:在常规临床实践中提高图像质量和检查时间的效用。

Compressed sensing and deep learning reconstruction for women's pelvic MRI denoising: Utility for improving image quality and examination time in routine clinical practice.

机构信息

Department of Radiology, Fujita Health University, School of Medicine, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan.

Department of Radiology, Fujita Health University, School of Medicine, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan; Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan.

出版信息

Eur J Radiol. 2021 Jan;134:109430. doi: 10.1016/j.ejrad.2020.109430. Epub 2020 Nov 21.

DOI:10.1016/j.ejrad.2020.109430
PMID:33276249
Abstract

PURPOSE

To demonstrate the utility of compressed sensing with parallel imaging (Compressed SPEEDER) and AiCE compared with that of conventional parallel imaging (SPEEDER) for shortening examination time and improving image quality of women's pelvic MRI.

METHOD

Thirty consecutive patients with women's pelvic diseases (mean age 50 years) underwent T2-weighted imaging using Compressed SPEEDER as well as conventional SPEEDER reconstructed with and without AiCE. The examination times were recorded, and signal-to-noise ratio (SNR) was calculated for every patient. Moreover, overall image quality was assessed using a 5-point scoring system, and final scores for all patients were determined by consensus of two readers. Mean examination time, SNR and overall image quality were compared among the four data sets by Wilcoxon signed-rank test.

RESULTS

Examination times for Compressed SPEEDER with and without AiCE were significantly shorter than those for conventional SPEEDER with and without AiCE (with AiCE: p < 0.0001, without AiCE: p < 0.0001). SNR of Compressed SPEEDER and of SPEEDER with AiCE was significantly superior to that of Compressed SPEEDER without AiCE (vs. Compressed SPEEDER, p = 0.01; vs. SPEEDER, p = 0.009). Overall image quality of Compressed SPEEDER with AiCE and of SPEEDER with and without AiCE was significantly higher than that of Compressed SPEEDER without AiCE (vs. Compressed SPEEDER with AiCE, p < 0.0001; vs. SPEEDER with AiCE, p < 0.0001; SPEEDER without AiCE, p = 0.0003).

CONCLUSION

Image quality and shorten examination time for T2-weighted imaging in women's pelvic MRI can be significantly improved by using Compressed SPEEDER with AiCE in comparison with conventional SPEEDER, although other sequences were not tested.

摘要

目的

展示压缩感知并行成像(Compressed SPEEDER)和 AiCE 与传统并行成像(SPEEDER)相比在缩短检查时间和提高女性盆腔 MRI 图像质量方面的效用。

方法

连续 30 例患有女性盆腔疾病的患者(平均年龄 50 岁)接受了 T2 加权成像,使用 Compressed SPEEDER 以及常规 SPEEDER 进行重建,同时使用和不使用 AiCE。记录检查时间,并计算每位患者的信噪比(SNR)。此外,使用 5 分制评分系统评估整体图像质量,由两位读者共识确定所有患者的最终评分。通过 Wilcoxon 符号秩检验比较四组数据集中的平均检查时间、SNR 和整体图像质量。

结果

Compressed SPEEDER 有和没有 AiCE 的检查时间明显短于常规 SPEEDER 有和没有 AiCE 的检查时间(有 AiCE:p<0.0001,无 AiCE:p<0.0001)。Compressed SPEEDER 和带 AiCE 的 SPEEDER 的 SNR 明显优于不带 AiCE 的 Compressed SPEEDER(与 Compressed SPEEDER 相比,p=0.01;与 SPEEDER 相比,p=0.009)。带 AiCE 的 Compressed SPEEDER 和带和不带 AiCE 的 SPEEDER 的整体图像质量明显高于不带 AiCE 的 Compressed SPEEDER(与带 AiCE 的 Compressed SPEEDER 相比,p<0.0001;与带 AiCE 的 SPEEDER 相比,p<0.0001;与不带 AiCE 的 SPEEDER 相比,p=0.0003)。

结论

与传统 SPEEDER 相比,使用带 AiCE 的 Compressed SPEEDER 可显著提高女性盆腔 MRI T2 加权成像的图像质量和缩短检查时间,尽管其他序列未进行测试。

相似文献

1
Compressed sensing and deep learning reconstruction for women's pelvic MRI denoising: Utility for improving image quality and examination time in routine clinical practice.压缩感知和深度学习重建在女性盆腔 MRI 去噪中的应用:在常规临床实践中提高图像质量和检查时间的效用。
Eur J Radiol. 2021 Jan;134:109430. doi: 10.1016/j.ejrad.2020.109430. Epub 2020 Nov 21.
2
Efficacy of compressed sensing and deep learning reconstruction for adult female pelvic MRI at 1.5 T.1.5T 成人女性盆腔 MRI 压缩感知与深度学习重建的效能。
Eur Radiol Exp. 2024 Sep 10;8(1):103. doi: 10.1186/s41747-024-00506-5.
3
MR imaging for shoulder diseases: Effect of compressed sensing and deep learning reconstruction on examination time and imaging quality compared with that of parallel imaging.磁共振成像在肩部疾病中的应用:压缩感知和深度学习重建对检查时间和成像质量的影响,与并行成像相比。
Magn Reson Imaging. 2022 Dec;94:56-63. doi: 10.1016/j.mri.2022.08.004. Epub 2022 Aug 5.
4
AI-assisted compressed sensing and parallel imaging sequences for MRI of patients with nasopharyngeal carcinoma: comparison of their capabilities in terms of examination time and image quality.人工智能辅助压缩感知和并行成像序列在鼻咽癌患者 MRI 中的应用:检查时间和图像质量方面的性能比较。
Eur Radiol. 2023 Nov;33(11):7686-7696. doi: 10.1007/s00330-023-09742-6. Epub 2023 May 23.
5
Compressed sensing and parallel imaging accelerated T2 FSE sequence for head and neck MR imaging: Comparison of its utility in routine clinical practice.压缩感知和并行成像加速 T2 FSE 序列在头颈部磁共振成像中的应用:在常规临床实践中的效用比较。
Eur J Radiol. 2021 Feb;135:109501. doi: 10.1016/j.ejrad.2020.109501. Epub 2020 Dec 28.
6
MR Imaging of Endolymphatic Hydrops: Utility of iHYDROPS-Mi2 Combined with Deep Learning Reconstruction Denoising.内淋巴积水的磁共振成像:iHYDROPS-Mi2 联合深度学习重建降噪的应用。
Magn Reson Med Sci. 2021 Sep 1;20(3):272-279. doi: 10.2463/mrms.mp.2020-0082. Epub 2020 Aug 21.
7
Hybrid deep-learning-based denoising method for compressed sensing in pituitary MRI: comparison with the conventional wavelet-based denoising method.基于混合深度学习的垂体 MRI 压缩感知去噪方法:与传统基于小波的去噪方法的比较。
Eur Radiol. 2022 Jul;32(7):4527-4536. doi: 10.1007/s00330-022-08552-6. Epub 2022 Feb 15.
8
Evaluation of a deep learning-based reconstruction method for denoising and image enhancement of shoulder MRI in patients with shoulder pain.评估一种基于深度学习的重建方法,用于对肩部疼痛患者的肩部 MRI 进行降噪和图像增强。
Eur Radiol. 2023 Jul;33(7):4875-4884. doi: 10.1007/s00330-023-09472-9. Epub 2023 Feb 18.
9
Combination Use of Compressed Sensing and Deep Learning for Shoulder Magnetic Resonance Imaging With Various Sequences.压缩感知与深度学习在不同序列肩部磁共振成像中的联合应用。
J Comput Assist Tomogr. 2023;47(2):277-283. doi: 10.1097/RCT.0000000000001418. Epub 2023 Mar 9.
10
Comparison of utility of deep learning reconstruction on 3D MRCPs obtained with three different k-space data acquisitions in patients with IPMN.对比三种不同的 k 空间采集方法在胰胆管乳头状黏液瘤患者中获得的 3D MRCP 上使用深度学习重建的效用。
Eur Radiol. 2022 Oct;32(10):6658-6667. doi: 10.1007/s00330-022-08877-2. Epub 2022 Jun 10.

引用本文的文献

1
Deep learning-based single-shot computational spectrometer using multilayer thin films.基于深度学习的使用多层薄膜的单镜头计算光谱仪。
Sci Rep. 2025 Jul 1;15(1):21232. doi: 10.1038/s41598-025-06691-6.
2
The value of Fast Dixon combined with deep learning technology in contrast agent-free high-resolution magnetic resonance imaging of the brachial plexus.快速狄克逊序列联合深度学习技术在臂丛神经无对比剂高分辨率磁共振成像中的价值
Front Neurol. 2025 Jun 4;16:1558927. doi: 10.3389/fneur.2025.1558927. eCollection 2025.
3
Deep learning reconstruction of diffusion-weighted imaging with single-shot echo-planar imaging in endometrial cancer: a comparison with multi-shot echo-planar imaging.
子宫内膜癌中基于单次激发回波平面成像的扩散加权成像深度学习重建:与多次激发回波平面成像的比较
Abdom Radiol (NY). 2025 Apr 18. doi: 10.1007/s00261-025-04955-3.
4
The scientific evidence of commercial AI products for MRI acceleration: a systematic review.用于磁共振成像加速的商用人工智能产品的科学证据:一项系统综述
Eur Radiol. 2025 Feb 19. doi: 10.1007/s00330-025-11423-5.
5
Accelerating veterinary low field MRI acquisitions using the deep learning based denoising solution HawkAI.使用基于深度学习的去噪解决方案HawkAI加速兽医低场MRI采集。
Sci Rep. 2025 Feb 18;15(1):5846. doi: 10.1038/s41598-025-88822-7.
6
Recent trends in scientific research in chest radiology: What to do or not to do? That is the critical question in research.胸部放射学科学研究的最新趋势:该做什么或不该做什么?这是研究中的关键问题。
Jpn J Radiol. 2025 Jan 16. doi: 10.1007/s11604-025-01735-3.
7
Image quality in three-dimensional (3D) contrast-enhanced dynamic magnetic resonance imaging of the abdomen using deep learning denoising technique: intraindividual comparison between T1-weighted sequences with compressed sensing and with a modified Fast 3D mode wheel.使用深度学习去噪技术的腹部三维(3D)对比增强动态磁共振成像中的图像质量:具有压缩感知的T1加权序列与改良快速3D模式轮之间的个体内比较。
Jpn J Radiol. 2025 Mar;43(3):455-462. doi: 10.1007/s11604-024-01687-0. Epub 2024 Nov 6.
8
Style harmonization of panoramic radiography using deep learning.基于深度学习的全景X线摄影图像风格协调
Oral Radiol. 2025 Jan;41(1):111-119. doi: 10.1007/s11282-024-00782-2. Epub 2024 Oct 29.
9
Efficacy of compressed sensing and deep learning reconstruction for adult female pelvic MRI at 1.5 T.1.5T 成人女性盆腔 MRI 压缩感知与深度学习重建的效能。
Eur Radiol Exp. 2024 Sep 10;8(1):103. doi: 10.1186/s41747-024-00506-5.
10
Recent trends in AI applications for pelvic MRI: a comprehensive review.人工智能在盆腔 MRI 中的应用研究进展:综述
Radiol Med. 2024 Sep;129(9):1275-1287. doi: 10.1007/s11547-024-01861-4. Epub 2024 Aug 3.