Fahrenholtz S, Fuentes D, Stafford R, Hazle J
The University of Texas MD Anderson Cancer Center, Houston, TX.
The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, TX.
Med Phys. 2012 Jun;39(6Part20):3857. doi: 10.1118/1.4735746.
Magnetic resonance-guided laser induced thermal therapy (MRgLITT) is a minimally invasive thermal treatment for metastatic brain lesions, offering an alternative to conventional surgery. The purpose of this investigation is to incorporate uncertainty quantification (UQ) into the biothermal parameters used in the Pennes bioheat transfer equation (BHT), in order to account for imprecise values available in the literature. The BHT is a partial differential equation commonly used in thermal therapy models.
MRgLITT was performed on an in vivo canine brain in a previous investigation. The canine MRgLITT was modeled using the BHT. The BHT has four parameters'" microperfusion, conductivity, optical absorption, and optical scattering'"which lack precise measurements in living brain and tumor. The uncertainties in the parameters were expressed as probability distribution functions derived from literature values. A univariate generalized polynomial chaos (gPC) expansion was applied to the stochastic BHT. The gPC approach to UQ provides a novel methodology to calculate spatio-temporal voxel-wise means and variances of the predicted temperature distributions. The performance of the gPC predictions were evaluated retrospectively by comparison with MR thermal imaging (MRTI) acquired during the MRgLITT procedure in the canine model. The comparison was evaluated with root mean square difference (RMSD), isotherm contours, spatial profiles, and z-tests.
The peak RMSD was ∼1.5 standard deviations for microperfusion, conductivity, and optical absorption, while optical scattering was ∼2.2 standard deviations. Isotherm contours and spatial profiles of the simulation's predicted mean plus or minus two standard deviations demonstrate the MRTI temperature was enclosed by the model's isotherm confidence interval predictions. An a = 0.01 z-test demonstrates agreement.
The application of gPC for UQ is a potentially powerful means for providing predictive simulations despite poorly known input parameters. gPC provides an output that represents the probable distribution of outcomes for MRgLITT.
磁共振引导激光诱导热疗（MRgLITT）是一种针对脑转移瘤的微创热治疗方法，为传统手术提供了一种替代方案。本研究的目的是将不确定性量化（UQ）纳入彭尼斯生物热传递方程（BHT）中使用的生物热参数，以考虑文献中可用的不精确值。BHT是热疗模型中常用的偏微分方程。
在先前的一项研究中，对犬活体脑进行了MRgLITT。使用BHT对犬MRgLITT进行建模。BHT有四个参数——微灌注、电导率、光吸收和光散射——在活体脑和肿瘤中缺乏精确测量。参数的不确定性表示为从文献值导出的概率分布函数。将单变量广义多项式混沌（gPC）展开应用于随机BHT。gPC方法用于UQ提供了一种新的方法来计算预测温度分布的时空体素均值和方差。通过与犬模型MRgLITT过程中获取的磁共振热成像（MRTI）进行比较，回顾性评估gPC预测的性能。使用均方根差（RMSD）、等温线轮廓、空间剖面和z检验进行比较评估。
微灌注、电导率和光吸收的峰值RMSD约为1.5个标准差，而光散射约为2.2个标准差。模拟预测均值加减两个标准差的等温线轮廓和空间剖面表明，MRTI温度被模型的等温线置信区间预测所包围。α = 0.01的z检验表明具有一致性。
尽管输入参数知之甚少，但将gPC应用于UQ是提供预测模拟的一种潜在有力手段。gPC提供了一个代表MRgLITT结果可能分布的输出。
