A steady-state MR fingerprinting sequence optimization framework applied to the fast 3D quantification of fat fraction and water T1 in the thigh muscles.

IF 3 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Magnetic Resonance in Medicine Pub Date : 2025-03-04 DOI:10.1002/mrm.30490
Constantin Slioussarenko, Pierre-Yves Baudin, Benjamin Marty
{"title":"A steady-state MR fingerprinting sequence optimization framework applied to the fast 3D quantification of fat fraction and water T1 in the thigh muscles.","authors":"Constantin Slioussarenko, Pierre-Yves Baudin, Benjamin Marty","doi":"10.1002/mrm.30490","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>The aim of this study was to develop an optimization framework to shorten GRE-based MRF sequences while keeping similar parameter estimation quality.</p><p><strong>Methods: </strong>An optimization framework taking into account steady-state initial longitudinal magnetization, undersampling artifacts, and mitigating overfitting by drawing from a realistic numerical thighs phantom database was developed and validated on numerical simulations and 10 healthy volunteers.</p><p><strong>Results: </strong>The sequences optimized with the proposed framework decreased the original sequence duration by 30% (8 s per repetition instead of 11.2 s) while showing improved accuracy (SSIM going up from 96% to 99% for <math> <semantics><mrow><mi>F</mi> <mi>F</mi></mrow> <annotation>$$ FF $$</annotation></semantics> </math> , from 93% to 96% for <math> <semantics><mrow><mi>T</mi> <msub><mrow><mn>1</mn></mrow> <mrow><mi>H</mi> <mn>2</mn> <mi>O</mi></mrow> </msub> </mrow> <annotation>$$ T{1}_{H2O} $$</annotation></semantics> </math> on numerical simulations) and precision, especially when compared with sequences optimized through other means.</p><p><strong>Conclusions: </strong>The proposed framework paves the way for fast 3D quantification of <math> <semantics><mrow><mi>F</mi> <mi>F</mi></mrow> <annotation>$$ FF $$</annotation></semantics> </math> and <math> <semantics><mrow><mi>T</mi> <msub><mrow><mn>1</mn></mrow> <mrow><mi>H</mi> <mn>2</mn> <mi>O</mi></mrow> </msub> </mrow> <annotation>$$ T{1}_{H2O} $$</annotation></semantics> </math> in the skeletal muscle.</p>","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":" ","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Magnetic Resonance in Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/mrm.30490","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0

Abstract

Purpose: The aim of this study was to develop an optimization framework to shorten GRE-based MRF sequences while keeping similar parameter estimation quality.

Methods: An optimization framework taking into account steady-state initial longitudinal magnetization, undersampling artifacts, and mitigating overfitting by drawing from a realistic numerical thighs phantom database was developed and validated on numerical simulations and 10 healthy volunteers.

Results: The sequences optimized with the proposed framework decreased the original sequence duration by 30% (8 s per repetition instead of 11.2 s) while showing improved accuracy (SSIM going up from 96% to 99% for F F $$ FF $$ , from 93% to 96% for T 1 H 2 O $$ T{1}_{H2O} $$ on numerical simulations) and precision, especially when compared with sequences optimized through other means.

Conclusions: The proposed framework paves the way for fast 3D quantification of F F $$ FF $$ and T 1 H 2 O $$ T{1}_{H2O} $$ in the skeletal muscle.

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
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
6.70
自引率
24.20%
发文量
376
审稿时长
2-4 weeks
期刊介绍: Magnetic Resonance in Medicine (Magn Reson Med) is an international journal devoted to the publication of original investigations concerned with all aspects of the development and use of nuclear magnetic resonance and electron paramagnetic resonance techniques for medical applications. Reports of original investigations in the areas of mathematics, computing, engineering, physics, biophysics, chemistry, biochemistry, and physiology directly relevant to magnetic resonance will be accepted, as well as methodology-oriented clinical studies.
期刊最新文献
3D MERMAID: 3D Multi-shot enhanced recovery motion artifact insensitive diffusion for submillimeter, multi-shell, and SNR-efficient diffusion imaging. A flexible MRF approach to improve kinetic rate estimation with bSSFP-based hyperpolarized [1-13C]pyruvate MRI. A steady-state MR fingerprinting sequence optimization framework applied to the fast 3D quantification of fat fraction and water T1 in the thigh muscles. Accelerating spin-echo EPI through combined patterned multislice excitation and simultaneous multislice acquisition. Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 2-Ex vivo imaging: Added value and acquisition.
×
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