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

目前用于癌性肿瘤的基于辐射的治疗的技术几乎完全依赖于诊断图像，例如MRI和PET扫描，以使得能够对多个高强度射束进行仔细的靶向。然而，辐射能量穿透到生物组织中的非常复杂的性质使得这种靶向困难且容易出错，甚至可能由于损害癌区域附近的基本组织的风险而禁止放射治疗。这类问题在脑癌的治疗中自然受到特别关注。本项目将研究开发一个模拟环境，最终将提供一个针对患者的感兴趣区域的虚拟模型，并能够准确有效地模拟在这种环境中的波传播，具有最终为放射专家提供在线工具的潜力，用于微调放射能量的目标，并且在将来的阶段，该项目将开发一个环境，包括（a）MRI数据的清理和分割，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异性体积模型，（c）生成高阶曲线有限元网格，（d）对电磁能量到体积模型中的穿透/折射的完全以及降阶建模，以及（e）感兴趣的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，将在体模数据和真实的MRI数据上进行广泛的测试，可能会添加人工噪声以探索鲁棒性。关键的开发将包括新的图像分割和清理算法，改进的材料模型，开发有效的高阶波传播精确计算方案，域截断的有效方法，以及可视化和数据提取的工具。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具体应用。

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.