Diffusion tensor imaging (DTI) is used in tumor growth models to provide information on the infiltration pathways of tumor cells into the surrounding brain tissue. When a patient-specific DTI is not available, a template image such as a DTI atlas can be transformed to the patient anatomy using image registration. This study investigates a model, the invariance under coordinate transform (ICT), that transforms diffusion tensors from a template image to the patient image, based on the principle that the tumor growth process can be mapped, at any point in time, between the images using the same transformation function that we use to map the anatomy. The ICT model allows the mapping of tumor cell densities and tumor fronts (as iso-levels of tumor cell density) from the template image to the patient image for inclusion in radiotherapy treatment planning. The proposed approach transforms the diffusion tensors to simulate tumor growth in locally deformed anatomy and outputs the tumor cell density distribution over time. The ICT model is validated in a cohort of ten brain tumor patients. Comparative analysis with the tumor cell density in the original template image shows that the ICT model accurately simulates tumor cell densities in the deformed image space. By creating radiotherapy target volumes as tumor fronts, this study provides a framework for more personalized radiotherapy treatment planning, without the use of additional imaging.

中文翻译：

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用于个性化放射治疗中目标体积的扩散张量变换


扩散张量成像（DTI）用于肿瘤生长模型，以提供有关肿瘤细胞浸润到周围脑组织的途径的信息。当患者特定的 DTI 不可用时，可以使用图像配准将模板图像（例如 DTI 图集）转换为患者的解剖结构。本研究研究了一种模型，即坐标变换下的不变性 (ICT)，该模型将扩散张量从模板图像转换为患者图像，其原理是肿瘤生长过程可以在图像之间的任何时间点进行映射使用与我们用来绘制解剖结构相同的变换函数。 ICT 模型允许将肿瘤细胞密度和肿瘤前沿（作为肿瘤细胞密度的等水平）从模板图像映射到患者图像，以包含在放射治疗计划中。所提出的方法转换扩散张量以模拟局部变形解剖结构中的肿瘤生长，并输出随时间变化的肿瘤细胞密度分布。 ICT 模型在 10 名脑肿瘤患者的队列中得到了验证。与原始模板图像中肿瘤细胞密度的对比分析表明，ICT模型准确地模拟了变形图像空间中的肿瘤细胞密度。通过创建放射治疗靶区作为肿瘤前沿，这项研究为更加个性化的放射治疗计划提供了一个框架，而无需使用额外的成像。