Purpose: Develop a treatment planning framework for neurosurgeons treating high-grade gliomas with LITT to minimize the learning curve and improve tumor thermal dose coverage. Methods: Deidentified patient images were segmented using the image segmentation software Materialize MIMICS©. Segmented images were imported into the commercial finite element analysis (FEA) software COMSOL Multiphysics© to perform bioheat transfer simulations. The laser probe was modeled as a cylindrical object with radius 0.7 mm and length 100 mm, with a constant beam diameter. A modeled laser probe was placed in the tumor in accordance with patient specific patient magnetic resonance temperature imaging (MRTi) data. The laser energy was modeled as a deposited beam heat source in the FEA software. Penne’s bioheat equation was used to model heat transfer in brain tissue. The cerebrospinal fluid (CSF) was modeled as a solid with convectively enhanced conductivity to capture heat sink effects. In this study, thermal damage-dependent blood perfusion was assessed. Pulsed laser heating was modeled based on patient treatment logs. The stationary heat source and pullback heat source techniques were modeled to compare the calculated tissue damage. The developed bioheat transfer model was compared to MRTi data obtained from a laser log during LITT procedures. The application builder module in COMSOL Multiphysics© was utilized to create a Graphical User Interface (GUI) for the treatment planning framework. Results: Simulations predicted increased thermal damage (10–15%) in the tumor for the pullback heat source approach compared with the stationary heat source. The model-predicted temperature profiles followed trends similar to those of the MRTi data. Simulations predicted partial tissue ablation in tumors proximal to the CSF ventricle. Conclusion: A mobile platform-based GUI for bioheat transfer simulation was developed to aid neurosurgeons in conveniently varying the simulation parameters according to a patient-specific treatment plan. The convective effects of the CSF should be modeled with heat sink effects for accurate LITT treatment planning.
目的：为神经外科医生开发一个用于治疗高级别胶质瘤的激光间质热疗（LITT）治疗计划框架，以缩短学习曲线并提高肿瘤热剂量覆盖范围。方法：使用图像分割软件Materialize MIMICS©对去标识化的患者图像进行分割。将分割后的图像导入商业有限元分析（FEA）软件COMSOL Multiphysics©中进行生物热传递模拟。激光探头被建模为半径0.7毫米、长度100毫米的圆柱体，具有恒定光束直径。根据患者特定的磁共振温度成像（MRTi）数据，将建模的激光探头置于肿瘤内。激光能量在FEA软件中被建模为沉积光束热源。采用Penne生物热方程模拟脑组织中的热传递。脑脊液（CSF）被建模为具有对流增强导热性的固体，以捕捉热沉效应。本研究评估了热损伤依赖性血液灌注。基于患者治疗记录对脉冲激光加热进行建模。对静止热源和回撤热源技术进行建模，以比较计算出的组织损伤。将开发的生物热传递模型与LITT手术期间从激光记录中获取的MRTi数据进行比较。利用COMSOL Multiphysics©中的应用程序构建器模块，为治疗计划框架创建图形用户界面（GUI）。结果：模拟预测，与静止热源相比，回撤热源方法在肿瘤中产生的热损伤增加（10-15%）。模型预测的温度分布趋势与MRTi数据相似。模拟预测靠近CSF脑室的肿瘤组织发生部分消融。结论：开发了一个基于移动平台的生物热传递模拟GUI，以帮助神经外科医生根据患者特定的治疗计划方便地调整模拟参数。为精确的LITT治疗计划，CSF的对流效应应结合热沉效应进行建模。
Development of a Treatment Planning Framework for Laser Interstitial Thermal Therapy (LITT)
肿瘤(癌症)患者之家