WE-C-116-10: Univariate, Multivariate, and Nonlinear Uncertainty Quantification for Magnetic Resonance-Guided Laser Induced Thermal Therapy
WE-C-116-10: Univariate, Multivariate, and Nonlinear Uncertainty Quantification for Magnetic Resonance-Guided Laser Induced Thermal Therapy
复制标题
WE-C-116-10：磁共振引导激光诱导热疗的单变量、多变量和非线性不确定性量化
DOI：
10.1118/1.4815572
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发表时间：
2013
期刊：
影响因子：
3.8
通讯作者：
Fahrenholtz S
中科院分区：
文献类型：
--
作者：
Fahrenholtz S
目的MR引导激光诱导热疗法（MRgLITT）是一种新兴的微创神经外科工具，正在探索作为运动障碍、放射性坏死和颅内转移等疾病的替代治疗方法。主要目标是减少与传统手术相关的并发症和正常组织发病率。正在研究计算模型以帮助未来的 LITT 规划；然而，不精确和非患者特定的参数知识会损害准确性。这项工作探索将随机 Pennes 生物传热方程 (BHT) 的温度输出的不确定性量化 (UQ) 纳入其中。方法使用五参数（灌注、热导率、光吸收、光散射）随机 BHT LITT 模型。参数被认为是均匀分布，其范围由文献值确定。采用广义多项式混沌 (gPC) 来计算 UQ 输出温度分布的时空、体素函数。使用单变量 gPC 在计算机中探索了线性和非线性模型中的 BHT 参数敏感性。使用多变量 gPC 对体内正常犬脑 (n=4) 中的体模和 MRgLITT 的 MR 热成像 (MRTI) 进行回顾性分析。报告了等温线、时间和线性曲线。结果单变量模拟表明，光学参数解释了大部分模型方差（峰值标准差：各向异性 3.75 °C、吸收 2.94 °C、散射 1.84 °C、电导率 1.42 °C 和灌注 0.94 °C）。线性模型方差包含非线性模型方差。多变量模拟的平均温度和 95% 置信区间与测得的加热有很好的相关性，即使在施加器附近也是如此。结论gPC 可以提供稳健且相对快速的 UQ，促进脑组织中有用的前瞻性 LITT 规划，尽管参数知识不精确。更快的线性模拟近似于非线性模拟，没有过多的方差。此外，通过仅包含最敏感的参数，以最小的精度损失减少了计算负担。后续工作包括将随机 BHT 应用于回顾性人脑肿瘤 LITT。
PurposeMR‐guided laser‐induced thermal therapy (MRgLITT) is an emerging minimally invasive neurosurgical tool being explored as a treatment alternative for conditions such as motion disorder, radiation necrosis, and intracranial metastases. The primary goal is to reduce complications and normal tissue morbidity associated with conventional surgery. Computational models are being investigated to aid prospective LITT planning; however, accuracy is undermined by imprecise and non‐patient specific knowledge of parameters. This work explores incorporating uncertainty quantification (UQ) of temperature output from the stochastic Pennes bioheat transfer equation (BHT).MethodsA five parameter (perfusion, thermal conductivity, optical absorption, optical scattering) stochastic BHT LITT model was used. Parameters were considered to be uniform distributions with ranges informed by literature values. Generalized polynomial chaos (gPC) was employed to calculate spatio‐temporal, voxel‐wise functions of the output temperature distributions for UQ. BHT parameter sensitivity in linear and nonlinear models was explored in silico using univariate gPC. Retrospective analysis of MR thermography (MRTI) from both phantom and MRgLITT in normal canine brain in vivo (n=4) was explored using multivariate gPC. Isotherms, temporal and linear profiles were reported.ResultsUnivariate simulations demonstrated that optical parameters explained the majority of model variance (peak standard deviation: anisotropy 3.75 °C, absorption 2.94 °C, scattering 1.84 °C, conductivity 1.42 °C, and perfusion 0.94 °C). Linear model variance enclosed nonlinear model variance. Mean temperature and 95% confidence interval from multivariate simulations correlated well with measured heating even near the applicator.ConclusiongPC may provide robust and relatively fast UQ facilitating useful prospective LITT planning in brain tissue despite imprecise knowledge of parameters. The faster linear simulation approximated the nonlinear simulation without excessive variance. Further, the computational burden was reduced with minimal accuracy loss by including only the most sensitive parameters. Subsequent work includes applying stochastic BHT to retrospective human brain tumor LITT.