Slioussarenko Constantin, Baudin Pierre-Yves, Marty Benjamin
Neuromuscular Investigation Center, NMR Laboratory, Institute of Myology, Paris Cedex 13, France.
Magn Reson Med. 2025 Jun;93(6):2623-2639. doi: 10.1002/mrm.30490. Epub 2025 Mar 4.
The aim of this study was to develop an optimization framework to shorten GRE-based MRF sequences while keeping similar parameter estimation quality.
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.
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.
The proposed framework paves the way for fast 3D quantification of and in the skeletal muscle.
本研究的目的是开发一种优化框架，以缩短基于梯度回波（GRE）的磁共振指纹（MRF）序列，同时保持相似的参数估计质量。
开发了一种优化框架，该框架考虑了稳态初始纵向磁化、欠采样伪影，并通过从真实的数值大腿模型数据库中提取数据来减轻过拟合。该框架在数值模拟和10名健康志愿者身上进行了验证。
用所提出的框架优化后的序列将原始序列持续时间减少了30%（每个重复从11.2秒减少到8秒），同时显示出更高的准确性（在数值模拟中，对于 ，结构相似性指数（SSIM）从96%提高到99%，对于 从93%提高到96%）和精度，特别是与通过其他方法优化的序列相比。
所提出的框架为骨骼肌中 和 的快速三维定量分析铺平了道路。
