Collaborative Research ITR/NGS: An Integrated Simulation Environment for High-Resolution Computational Methods in Electromagnetics with Biomedical Applications

目前基于放射治疗癌症肿瘤的技术几乎完全依赖于诊断图像，如MRI和PET扫描，以确保多个高强度光束的精确定位。然而，辐射能量渗透到生物组织的非常复杂的性质使得这种靶向变得困难和容易出错，甚至可能禁止放射治疗，因为有可能损害靠近癌变区域的基本组织。这些问题在脑癌的治疗中自然是特别值得关注的。该项目将对模拟环境的开发进行研究，最终将提供感兴趣领域的虚拟患者特定模型，以及在这种环境中准确有效地模拟波传播的能力，并有可能最终为辐射专家提供在线工具，用于微调辐射能量的目标，并在未来阶段，甚至可以模拟能量沉积和热量释放对组织的影响。该项目将开发一个环境，包括(a) MRI数据的清洗和分割，包括具有噪声敏感性的数据，(b)提取材料数据并构建感兴趣目标的患者特定体积模型，(c)生成高阶，曲线，有限元网格，(d)电磁能量渗透/折射到体积模型的全阶和低阶建模。(e)感兴趣的生理数据的可视化和提取。这些不同的元素将集成到一个灵活的、独立的环境中，作为开发的一部分，将在虚拟数据和真实MRI数据上进行广泛的测试，可能会添加人工噪声来探索鲁棒性。关键的发展将包括新的图像分割和清理算法，改进的材料模型，开发高效的高阶精确的波传播计算方案，有效的域截断方法，以及可视化和数据提取工具。这些都是具有普遍重要性的问题，其潜在影响远远超出所考虑的特定应用。

Current technologies for radiationbased treatment of cancerous tumors rely almost exclusively on diagnosticimages such as MRI and PET scans to enable a careful targeting of multiple high-intensity beams.However, the very complex nature of the penetration of radiation energy into the biological tissue makes such targeting difficult and error-prone, possibly even prohibiting radiative treatment due to the risk of damaging essential tissue in close proximity to the cancerous areas. Such issues are naturally of particular concern in relation to treatment of brain cancer.This project will conduct research on the development of a simulation environment which eventually will provide a virtual patient-specific model of the area of interest and an ability to accurately and efficiently model wave-propagation within such an environment, with the potential to ultimately provide the radiation specialist with an online tool for fine tuning the targeting of radiation energy and, at a future stage, perhaps even model the impact of the energy deposition and heat release on the tissue.The project will develop an environment comprising of (a) the cleaning and segmentation of MRI data, including data with noise sensitivity, (b) extraction of material data and construction of a patient-specific volume model of the target of interest, (c) the generation of high-order, curvilinear, finite elements grids, (d) full as well as reduced order modeling of the penetration/refraction of electromagnetic energy into the volume model, and (e) visualization and extraction of physiological data of interest. These different elements will be integrated into a flexible, stand-alone environment and will, as part of the development, be tested extensively on phantom data as well as real MRI data, possibly with added artificial noiseto explore robustness.The key developments will include new image segmentation and cleaning algorithms, improved material models, the development of efficient high-order accurate computational schemes for wave-propagation, efficient methods for domain truncation, and tools for visualization and data extraction. These are all problems of generic importance with potential for impact well beyond the particular application being considered.