Geometric Reliability of Super-Resolution Reconstructed Images from Clinical Fetal MRI in the Second Trimester.

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Neuroinformatics Pub Date : 2023-07-01 Epub Date: 2023-06-07 DOI:10.1007/s12021-023-09635-5
Tommaso Ciceri, Letizia Squarcina, Alessandro Pigoni, Adele Ferro, Florian Montano, Alessandra Bertoldo, Nicola Persico, Simona Boito, Fabio Maria Triulzi, Giorgio Conte, Paolo Brambilla, Denis Peruzzo
{"title":"Geometric Reliability of Super-Resolution Reconstructed Images from Clinical Fetal MRI in the Second Trimester.","authors":"Tommaso Ciceri,&nbsp;Letizia Squarcina,&nbsp;Alessandro Pigoni,&nbsp;Adele Ferro,&nbsp;Florian Montano,&nbsp;Alessandra Bertoldo,&nbsp;Nicola Persico,&nbsp;Simona Boito,&nbsp;Fabio Maria Triulzi,&nbsp;Giorgio Conte,&nbsp;Paolo Brambilla,&nbsp;Denis Peruzzo","doi":"10.1007/s12021-023-09635-5","DOIUrl":null,"url":null,"abstract":"<p><p>Fetal Magnetic Resonance Imaging (MRI) is an important noninvasive diagnostic tool to characterize the central nervous system (CNS) development, significantly contributing to pregnancy management. In clinical practice, fetal MRI of the brain includes the acquisition of fast anatomical sequences over different planes on which several biometric measurements are manually extracted. Recently, modern toolkits use the acquired two-dimensional (2D) images to reconstruct a Super-Resolution (SR) isotropic volume of the brain, enabling three-dimensional (3D) analysis of the fetal CNS.We analyzed 17 fetal MR exams performed in the second trimester, including orthogonal T2-weighted (T2w) Turbo Spin Echo (TSE) and balanced Fast Field Echo (b-FFE) sequences. For each subject and type of sequence, three distinct high-resolution volumes were reconstructed via NiftyMIC, MIALSRTK, and SVRTK toolkits. Fifteen biometric measurements were assessed both on the acquired 2D images and SR reconstructed volumes, and compared using Passing-Bablok regression, Bland-Altman plot analysis, and statistical tests.Results indicate that NiftyMIC and MIALSRTK provide reliable SR reconstructed volumes, suitable for biometric assessments. NiftyMIC also improves the operator intraclass correlation coefficient on the quantitative biometric measures with respect to the acquired 2D images. In addition, TSE sequences lead to more robust fetal brain reconstructions against intensity artifacts compared to b-FFE sequences, despite the latter exhibiting more defined anatomical details.Our findings strengthen the adoption of automatic toolkits for fetal brain reconstructions to perform biometry evaluations of fetal brain development over common clinical MR at an early pregnancy stage.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"21 3","pages":"549-563"},"PeriodicalIF":2.7000,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406722/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neuroinformatics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s12021-023-09635-5","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/6/7 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

Abstract

Fetal Magnetic Resonance Imaging (MRI) is an important noninvasive diagnostic tool to characterize the central nervous system (CNS) development, significantly contributing to pregnancy management. In clinical practice, fetal MRI of the brain includes the acquisition of fast anatomical sequences over different planes on which several biometric measurements are manually extracted. Recently, modern toolkits use the acquired two-dimensional (2D) images to reconstruct a Super-Resolution (SR) isotropic volume of the brain, enabling three-dimensional (3D) analysis of the fetal CNS.We analyzed 17 fetal MR exams performed in the second trimester, including orthogonal T2-weighted (T2w) Turbo Spin Echo (TSE) and balanced Fast Field Echo (b-FFE) sequences. For each subject and type of sequence, three distinct high-resolution volumes were reconstructed via NiftyMIC, MIALSRTK, and SVRTK toolkits. Fifteen biometric measurements were assessed both on the acquired 2D images and SR reconstructed volumes, and compared using Passing-Bablok regression, Bland-Altman plot analysis, and statistical tests.Results indicate that NiftyMIC and MIALSRTK provide reliable SR reconstructed volumes, suitable for biometric assessments. NiftyMIC also improves the operator intraclass correlation coefficient on the quantitative biometric measures with respect to the acquired 2D images. In addition, TSE sequences lead to more robust fetal brain reconstructions against intensity artifacts compared to b-FFE sequences, despite the latter exhibiting more defined anatomical details.Our findings strengthen the adoption of automatic toolkits for fetal brain reconstructions to perform biometry evaluations of fetal brain development over common clinical MR at an early pregnancy stage.

Abstract Image

Abstract Image

Abstract Image

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
第二个月临床胎儿MRI超分辨率重建图像的几何可靠性。
胎儿磁共振成像(MRI)是表征中枢神经系统(CNS)发育的一种重要的非侵入性诊断工具,对妊娠管理有重要贡献。在临床实践中,胎儿大脑MRI包括在不同平面上采集快速解剖序列,在这些序列上手动提取几个生物特征测量值。最近,现代工具包使用采集的二维(2D)图像重建大脑的超分辨率(SR)各向同性体积,从而能够对胎儿中枢神经系统进行三维(3D)分析。我们分析了妊娠中期进行的17次胎儿MR检查,包括正交T2加权(T2w)涡轮自旋回波(TSE)和平衡快速场回波(b-FFE)序列。对于每种受试者和序列类型,通过NiftyMIC、MIALSRTK和SVRTK工具包重建三个不同的高分辨率体积。在采集的2D图像和SR重建体积上评估了15个生物特征测量,并使用Passing Bablok回归、Bland-Altman图分析和统计检验进行了比较。结果表明,NiftyMIC和MIALSRTK提供了可靠的SR重建体积,适用于生物特征评估。NiftyMIC还提高了操作员关于所采集的2D图像的定量生物特征测量的组内相关系数。此外,与b-FFE序列相比,TSE序列能够针对强度伪影进行更稳健的胎儿大脑重建,尽管后者表现出更明确的解剖细节。我们的研究结果加强了胎儿大脑重建自动化工具包的采用,以在妊娠早期对胎儿大脑发育进行生物测量评估,而不是常规临床MR。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Neuroinformatics
Neuroinformatics 医学-计算机:跨学科应用
CiteScore
6.00
自引率
6.70%
发文量
54
审稿时长
3 months
期刊介绍: Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.
期刊最新文献
Teaching Research Data Management with DataLad: A Multi-year, Multi-domain Effort. Hands-On Neuroinformatics Education at the Crossroads of Online and In-Person: Lessons Learned from NeuroHackademy. Utilizing fMRI to Guide TMS Targets: the Reliability and Sensitivity of fMRI Metrics at 3 T and 1.5 T. Bayesian Tensor Modeling for Image-based Classification of Alzheimer's Disease. A Bayesian Multiplex Graph Classifier of Functional Brain Connectivity Across Diverse Tasks of Cognitive Control.
×
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