Collins Jarrod A, Heiselman Jon S, Clements Logan W, Weis Jared A, Brown Daniel B, Miga Michael I
IEEE Trans Biomed Eng. 2020 Jun;67(6):1548-1557. doi: 10.1109/TBME.2019.2939686. Epub 2019 Sep 5.
Accurate prospective modeling of microwave ablation (MWA) procedures can provide powerful planning and navigational information to physicians. However, patient-specific tissue properties are generally unavailable and can vary based on factors such as relative perfusion and state of disease. Therefore, a need exists for modeling frameworks that account for variations in tissue properties.
In this study, we establish an inverse modeling approach to reconstruct a set of tissue properties that best fit the model-predicted and observed ablation zone extents in a series of phantoms of varying fat content. We then create a model of these tissue properties as a function of fat content and perform a comprehensive leave-one-out evaluation of the predictive property model. Furthermore, we validate the inverse-model predictions in a separate series of phantoms that include co-recorded temperature data.
This model-based approach yielded thermal profiles in close agreement with experimental measurements in the series of validation phantoms (average root-mean-square error of 4.8 °C). The model-predicted ablation zones showed compelling overlap with observed ablations in both the series of validation phantoms (93.4 ± 2.2%) and the leave-one-out cross validation study (86.6 ± 5.3%). These results demonstrate an average improvement of 17.3% in predicted ablation zone overlap when comparing the presented property-model to properties derived from phantom component volume fractions.
These results demonstrate accurate model-predicted ablation estimates based on image-driven determination of tissue properties.
The work demonstrates, as a proof-of-concept, that physical modeling parameters can be linked with quantitative medical imaging to improve the utility of predictive procedural modeling for MWA.
对微波消融（MWA）过程进行精确的前瞻性建模可为医生提供有力的规划和导航信息。然而，特定患者的组织特性通常难以获取，并且会因相对灌注和疾病状态等因素而有所不同。因此，需要能够考虑组织特性变化的建模框架。
在本研究中，我们建立了一种逆建模方法，以重建一组最能拟合一系列不同脂肪含量体模中模型预测和观察到的消融区范围的组织特性。然后，我们创建一个这些组织特性作为脂肪含量函数的模型，并对预测特性模型进行全面的留一法评估。此外，我们在另一系列包括同步记录温度数据的体模中验证逆模型预测。
这种基于模型的方法产生的热分布与验证体模系列中的实验测量结果高度吻合（平均均方根误差为4.8°C）。在验证体模系列（93.4±2.2%）和留一法交叉验证研究（86.6±5.3%）中，模型预测的消融区与观察到的消融区都有显著重叠。与从体模成分体积分数得出的特性相比，当将所提出的特性模型用于预测时，消融区重叠预测平均提高了17.3%。
这些结果表明基于图像驱动的组织特性测定能够准确地进行模型预测的消融估计。
作为概念验证，该研究表明物理建模参数可与定量医学成像相联系，以提高MWA预测性程序建模的效用。
