• 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

使用压缩感知技术加速儿科脑成像的 3D T2 加权图像。

Accelerated 3D T2-weighted images using compressed sensing for pediatric brain imaging.

机构信息

Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.

Siemens Healthineers Ltd, Seoul, Korea.

出版信息

Neuroradiology. 2022 Dec;64(12):2399-2407. doi: 10.1007/s00234-022-03028-2. Epub 2022 Aug 3.

DOI:10.1007/s00234-022-03028-2
PMID:35920890
Abstract

PURPOSE

The purpose of this study was to compare the image quality of the 3D T2-weighted images accelerated using conventional method (CAI-SPACE) with the images accelerated using compressed sensing (CS-SPACE) in pediatric brain imaging.

METHODS

A total of 116 brain MRI (53 with CAI-SPACE and 63 with CS-SPACE) were obtained from children 16 years old or younger. Quantitative image quality was evaluated using the apparent signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). The sequences were qualitatively evaluated for overall image quality, general artifact, cerebrospinal fluid (CSF)-related artifact, and grey-white matter differentiation. The two sequences were compared for the total and two age groups (< 24 months vs. ≥ 24 months).

RESULTS

Compressed sensing application in 3D T2-weighted imaging resulted in 8.5% reduction in scanning time. Quantitative image quality analysis showed higher apparent SNR (median [Interquartile range]; 29 [25] vs. 23 [14], P = 0.005) and CNR (0.231 [0.121] vs. 0.165 [0.120], P = 0.027) with CS-SPACE compared to CAI-SPACE. Qualitative image quality analysis showed better image quality with CS-SPACE for general (P = 0.024) and CSF-related artifact (P < 0.001). CSF-related artifacts reduction was prominent in the older age group (≥ 24 months). Overall image quality (P = 0.162) and grey-white matter differentiation (P = 0.397) were comparable between CAI-SPACE and CS-SPACE.

CONCLUSION

Compressed sensing application in 3D T2-weighted images modestly reduced acquisition time and lowered CSF-related artifact compared to conventional images of the pediatric brain.

摘要

目的

本研究旨在比较使用常规方法(CAI-SPACE)加速和使用压缩感知(CS-SPACE)加速的儿童脑部成像 3D T2 加权图像的图像质量。

方法

共获得 116 例年龄在 16 岁及以下儿童的脑部 MRI(53 例采用 CAI-SPACE,63 例采用 CS-SPACE)。使用表观信噪比(SNR)和对比噪声比(CNR)评估定量图像质量。对总体图像质量、一般伪影、脑脊液(CSF)相关伪影和灰白质区分进行定性评估。比较两种序列的总扫描时间和两个年龄组(<24 个月和≥24 个月)的扫描时间。

结果

3D T2 加权成像中应用压缩感知可使扫描时间减少 8.5%。定量图像质量分析显示,CS-SPACE 的表观 SNR(中位数[四分位数间距];29[25]比 23[14],P=0.005)和 CNR(0.231[0.121]比 0.165[0.120],P=0.027)均高于 CAI-SPACE。定性图像质量分析显示,CS-SPACE 的总体(P=0.024)和 CSF 相关伪影(P<0.001)质量更好。在年龄较大的组(≥24 个月)中,CSF 相关伪影减少更为明显。总体图像质量(P=0.162)和灰白质区分(P=0.397)在 CAI-SPACE 和 CS-SPACE 之间无差异。

结论

与儿童脑部常规图像相比,3D T2 加权图像中应用压缩感知可适度减少采集时间,并降低 CSF 相关伪影。

相似文献

1
Accelerated 3D T2-weighted images using compressed sensing for pediatric brain imaging.使用压缩感知技术加速儿科脑成像的 3D T2 加权图像。
Neuroradiology. 2022 Dec;64(12):2399-2407. doi: 10.1007/s00234-022-03028-2. Epub 2022 Aug 3.
2
Compressed Sensing-Sensitivity Encoding (CS-SENSE) Accelerated Brain Imaging: Reduced Scan Time without Reduced Image Quality.压缩感知-灵敏度编码(CS-SENSE)加速脑成像:在不降低图像质量的情况下减少扫描时间。
AJNR Am J Neuroradiol. 2019 Jan;40(1):92-98. doi: 10.3174/ajnr.A5905. Epub 2018 Dec 6.
3
T2 Turbo Spin Echo With Compressed Sensing and Propeller Acquisition (Sampling k-Space by Utilizing Rotating Blades) for Fast and Motion Robust Prostate MRI: Comparison With Conventional Acquisition.T2 涡轮自旋回波压缩感知和螺旋桨采集(利用旋转叶片对采样 k 空间进行采样)用于快速和运动稳健的前列腺 MRI:与常规采集的比较。
Invest Radiol. 2023 Mar 1;58(3):209-215. doi: 10.1097/RLI.0000000000000923. Epub 2022 Sep 2.
4
Compressed Sensing SEMAC: 8-fold Accelerated High Resolution Metal Artifact Reduction MRI of Cobalt-Chromium Knee Arthroplasty Implants.压缩感知SEMAC:钴铬膝关节置换植入物的8倍加速高分辨率金属伪影减少磁共振成像
Invest Radiol. 2016 Oct;51(10):666-76. doi: 10.1097/RLI.0000000000000317.
5
Non-contrast enhancement of brachial plexus magnetic resonance imaging with compressed sensing.磁共振压缩感知技术在臂丛神经成像中的非增强扫描。
Eur J Radiol. 2023 Aug;165:110890. doi: 10.1016/j.ejrad.2023.110890. Epub 2023 May 23.
6
Evaluation of 3D T1-weighted spoiled gradient echo MR image quality using artificial intelligence image reconstruction techniques in the pediatric brain.基于人工智能图像重建技术的儿科脑 3D T1 加权扰相梯度回波磁共振图像质量评估
Neuroradiology. 2024 Oct;66(10):1849-1857. doi: 10.1007/s00234-024-03417-9. Epub 2024 Jul 5.
7
Three-Dimensional CAIPIRINHA SPACE TSE for 5-Minute High-Resolution MRI of the Knee.用于膝关节5分钟高分辨率MRI的三维CAIPIRINHA空间快速自旋回波序列
Invest Radiol. 2016 Oct;51(10):609-17. doi: 10.1097/RLI.0000000000000287.
8
Hybrid of Compressed Sensing and Parallel Imaging Applied to Three-dimensional Isotropic T-weighted Turbo Spin-echo MR Imaging of the Lumbar Spine.应用于腰椎脊柱三维各向同性 T1 加权涡轮自旋回波磁共振成像的压缩感知与并行成像的混合。
Magn Reson Med Sci. 2020 Feb 10;19(1):48-55. doi: 10.2463/mrms.mp.2018-0132. Epub 2019 Mar 15.
9
Reconstruction of shoulder MRI using deep learning and compressed sensing: a validation study on healthy volunteers.深度学习和压缩感知在肩部 MRI 重建中的应用:一项对健康志愿者的验证研究。
Eur Radiol Exp. 2023 Oct 26;7(1):66. doi: 10.1186/s41747-023-00377-2.
10
Comparison of compressed sensing-sensitivity encoding (CS-SENSE) accelerated 3D T2W TSE sequence versus conventional 3D and 2D T2W TSE sequences in rectal cancer: a prospective study.对比压缩感知敏感编码(CS-SENSE)加速 3D T2W TSE 序列与常规 3D 和 2D T2W TSE 序列在直肠癌中的应用:一项前瞻性研究。
Abdom Radiol (NY). 2022 Nov;47(11):3660-3670. doi: 10.1007/s00261-022-03636-9. Epub 2022 Aug 23.

引用本文的文献

1
Practical brain MRI guidelines for anti-Aβ antibody treatment in early symptomatic Alzheimer's disease.早期有症状阿尔茨海默病抗Aβ抗体治疗的实用脑MRI指南
Jpn J Radiol. 2025 Apr 23. doi: 10.1007/s11604-025-01773-x.

本文引用的文献

1
MRI Techniques to Decrease Imaging Times in Children.MRI 技术在儿童成像时间方面的应用
Radiographics. 2020 Mar-Apr;40(2):485-502. doi: 10.1148/rg.2020190112. Epub 2020 Feb 7.
2
Quantitative assessment of myelination patterns in preterm neonates using T2-weighted MRI.使用 T2 加权 MRI 对早产儿髓鞘形成模式进行定量评估。
Sci Rep. 2019 Sep 10;9(1):12938. doi: 10.1038/s41598-019-49350-3.
3
Magnetic Resonance Imaging of the Brain Using Compressed Sensing - Quality Assessment in Daily Clinical Routine.使用压缩感知的脑部磁共振成像——日常临床常规中的质量评估
Clin Neuroradiol. 2020 Jun;30(2):279-286. doi: 10.1007/s00062-019-00789-x. Epub 2019 May 16.
4
High-Resolution Magnetic Resonance Imaging Using Compressed Sensing for Intracranial and Extracranial Arteries: Comparison with Conventional Parallel Imaging.基于压缩感知的颅内和颅外动脉高分辨率磁共振成像:与常规并行成像的比较。
Korean J Radiol. 2019 Mar;20(3):487-497. doi: 10.3348/kjr.2018.0424.
5
Compressed Sensing-Sensitivity Encoding (CS-SENSE) Accelerated Brain Imaging: Reduced Scan Time without Reduced Image Quality.压缩感知-灵敏度编码(CS-SENSE)加速脑成像:在不降低图像质量的情况下减少扫描时间。
AJNR Am J Neuroradiol. 2019 Jan;40(1):92-98. doi: 10.3174/ajnr.A5905. Epub 2018 Dec 6.
6
Common artefacts encountered on images acquired with combined compressed sensing and SENSE.在采用联合压缩感知和并行采集技术(SENSE)获取的图像上常见的伪影。
Insights Imaging. 2018 Dec;9(6):1107-1115. doi: 10.1007/s13244-018-0668-4. Epub 2018 Nov 8.
7
Clinical Evaluation of Highly Accelerated Compressed Sensing Time-of-Flight MR Angiography for Intracranial Arterial Stenosis.高加速压缩感知时间飞跃磁共振血管成像技术在颅内动脉狭窄中的临床评估。
AJNR Am J Neuroradiol. 2018 Oct;39(10):1833-1838. doi: 10.3174/ajnr.A5786. Epub 2018 Sep 13.
8
Optimization of magnetization-prepared rapid gradient echo (MP-RAGE) sequence for neonatal brain MRI.用于新生儿脑部磁共振成像的磁化准备快速梯度回波(MP-RAGE)序列的优化
Pediatr Radiol. 2018 Aug;48(8):1139-1151. doi: 10.1007/s00247-018-4140-x. Epub 2018 May 2.
9
The UNC/UMN Baby Connectome Project (BCP): An overview of the study design and protocol development.北卡罗来纳大学教堂山分校/明尼苏达大学双城分校婴儿连接组计划(BCP):研究设计和方案制定概述。
Neuroimage. 2019 Jan 15;185:891-905. doi: 10.1016/j.neuroimage.2018.03.049. Epub 2018 Mar 22.
10
Image reconstruction by domain-transform manifold learning.基于域变换流形学习的图像重建。
Nature. 2018 Mar 21;555(7697):487-492. doi: 10.1038/nature25988.