Constantin Slioussarenko, Pierre-Yves Baudin, Benjamin Marty
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99% for <span></span><math>\n <semantics>\n <mrow>\n <mi>F</mi>\n <mi>F</mi>\n </mrow>\n <annotation>$$ FF $$</annotation>\n </semantics></math>, from 93% to 96% for <span></span><math>\n <semantics>\n <mrow>\n <mi>T</mi>\n <msub>\n <mrow>\n <mn>1</mn>\n </mrow>\n <mrow>\n <mi>H</mi>\n <mn>2</mn>\n <mi>O</mi>\n </mrow>\n </msub>\n </mrow>\n <annotation>$$ T{1}_{H2O} $$</annotation>\n </semantics></math> on numerical simulations) and precision, especially when compared with sequences optimized through other means.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>The proposed framework paves the way for fast 3D quantification of <span></span><math>\n <semantics>\n <mrow>\n <mi>F</mi>\n <mi>F</mi>\n </mrow>\n <annotation>$$ FF $$</annotation>\n </semantics></math> and <span></span><math>\n <semantics>\n <mrow>\n <mi>T</mi>\n <msub>\n <mrow>\n <mn>1</mn>\n </mrow>\n <mrow>\n <mi>H</mi>\n <mn>2</mn>\n <mi>O</mi>\n </mrow>\n </msub>\n </mrow>\n <annotation>$$ T{1}_{H2O} $$</annotation>\n </semantics></math> in the skeletal muscle.</p>\n </section>\n </div>","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":"93 6","pages":"2623-2639"},"PeriodicalIF":3.0000,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mrm.30490","citationCount":"0","resultStr":"{\"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\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Purpose</h3>\\n \\n <p>The aim of this study was to develop an optimization framework to shorten GRE-based MRF sequences while keeping similar parameter estimation quality.</p>\\n </section>\\n \\n 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引用次数: 0
摘要
目的:本研究的目的是建立一个优化框架,以缩短基于gre的MRF序列,同时保持相似的参数估计质量。方法:开发了一个优化框架,该框架考虑了稳态初始纵向磁化、欠采样伪影和通过绘制真实的数值大腿模型数据库来减轻过拟合,并在数值模拟和10名健康志愿者身上进行了验证。结果:采用该框架优化后的序列长度比原序列缩短了30倍% (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.
A steady-state MR fingerprinting sequence optimization framework applied to the fast 3D quantification of fat fraction and water T1 in the thigh muscles
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 , from 93% to 96% for 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 and in the skeletal muscle.
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.