Wang Xi, Huang Shao Ying, Yucel Abdulkadir C
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore.
Engineering Product Development Department, Singapore University of Technology and Design, Singapore 487372, Singapore.
Bioengineering (Basel). 2024 Jul 18;11(7):730. doi: 10.3390/bioengineering11070730.
As magnetic field strength in Magnetic Resonance Imaging (MRI) technology increases, maintaining the specific absorption rate (SAR) within safe limits across human head tissues becomes challenging due to the formation of standing waves at a shortened wavelength. Compounding this challenge is the uncertainty in the dielectric properties of head tissues, which notably affects the SAR induced by the radiofrequency (RF) coils in an ultra-high-field (UHF) MRI system. To this end, this study introduces a computational framework to quantify the impacts of uncertainties in head tissues' dielectric properties on the induced SAR. The framework employs a surrogate model-assisted Monte Carlo (MC) technique, efficiently generating surrogate models of MRI observables (electric fields and SAR) and utilizing them to compute SAR statistics. Particularly, the framework leverages a high-dimensional model representation technique, which constructs the surrogate models of the MRI observables via univariate and bivariate component functions, approximated through generalized polynomial chaos expansions. The numerical results demonstrate the efficiency of the proposed technique, requiring significantly fewer deterministic simulations compared with traditional MC methods and other surrogate model-assisted MC techniques utilizing machine learning algorithms, all while maintaining high accuracy in SAR statistics. Specifically, the proposed framework constructs surrogate models of a local SAR with an average relative error of 0.28% using 289 simulations, outperforming the machine learning-based surrogate modeling techniques considered in this study. Furthermore, the SAR statistics obtained by the proposed framework reveal fluctuations of up to 30% in SAR values within specific head regions. These findings highlight the critical importance of considering dielectric property uncertainties to ensure MRI safety, particularly in 7 T MRI systems.
随着磁共振成像（MRI）技术中磁场强度的增加，由于在缩短的波长下形成驻波，要将人体头部组织的比吸收率（SAR）维持在安全限度内变得颇具挑战性。使这一挑战更为复杂的是头部组织介电特性的不确定性，这尤其会影响超高频（UHF）MRI系统中射频（RF）线圈所诱发的SAR。为此，本研究引入了一个计算框架，以量化头部组织介电特性的不确定性对诱发SAR的影响。该框架采用代理模型辅助蒙特卡罗（MC）技术，高效生成MRI可观测量（电场和SAR）的代理模型，并利用它们来计算SAR统计数据。具体而言，该框架利用了一种高维模型表示技术，通过单变量和双变量分量函数构建MRI可观测量的代理模型，这些函数通过广义多项式混沌展开进行近似。数值结果证明了所提技术的效率，与传统MC方法以及其他利用机器学习算法的代理模型辅助MC技术相比，所需的确定性模拟显著减少，同时在SAR统计方面保持了高精度。具体来说，所提框架使用289次模拟构建了局部SAR的代理模型，平均相对误差为0.28%，优于本研究中考虑的基于机器学习的代理建模技术。此外，所提框架获得的SAR统计数据显示，特定头部区域内的SAR值波动高达30%。这些发现凸显了考虑介电特性不确定性以确保MRI安全的至关重要性，尤其是在7T MRI系统中。
