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

目前的癌症放射治疗技术几乎完全依赖于MRI和PET扫描等诊断图像来实现对多个高强度光束的仔细靶向。然而，辐射能量穿透到生物组织的非常复杂的性质使得这种靶向困难且容易出错，甚至可能由于损害靠近癌症区域的基本组织的风险而禁止放射治疗。这类问题自然是与脑癌治疗有关的特别令人关注的问题。该项目将对模拟环境的开发进行研究，该模拟环境最终将提供感兴趣区域的虚拟患者专用模型以及准确和高效地模拟这种环境中的波传播的能力，最终有可能为辐射专家提供用于微调辐射能量的定向的在线工具，并且在未来阶段，甚至可能模拟能量沉积和热释放对组织的影响。该项目将开发包括以下内容的环境：(A)MRI数据的清理和分割，包括具有噪声敏感性的数据，(B)材料数据的提取和感兴趣目标患者特定体积模型的构建；(C)高阶、曲线、有限元网格的生成；(D)电磁能量对体积模型的渗透/折射的全阶和降阶建模；以及(E)感兴趣生理数据的可视化和提取。这些不同的元素将被集成到一个灵活的、独立的环境中，作为开发的一部分，将在模型数据和真实MRI数据上进行广泛测试，可能会添加人工噪声来探索健壮性。关键开发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波传播计算方案、高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，其影响可能远远超出所审议的特定应用。

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.