CT Image Estimation for Calculation of Delivered Dose

用于计算输送剂量的 CT 图像估计

基本信息

批准号：
7747901
负责人：
EDWARD L. CHANEY
金额：
$ 15万
依托单位：
MORPHORMICS, INC.
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-08-01 至 2011-07-31
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): During radiation therapy for prostate cancer it is common practice to localize the prostate on many, ideally all, days of treatment to achieve the goal of delivering a high dose to the prostate while greatly sparing nearby radiosensitive normal tissues. The prostate can be localized in CT images acquired immediately prior to treatment or by tracking markers, implanted in the prostate, during treatment delivery. The advantage of CT imaging is that the image data provides the basis for computing radiation doses actually delivered to the prostate and surrounding tissues as needed for Adaptive Radiation Therapy (ART), a procedure for periodically adjusting the treatment plan in order to deliver a final dose distribution as originally planned. Disadvantages of CT imaging include monetary, space and time expenses. Major advantages of marker tracking include ease of use and frequent sampling during each dose fraction, e.g., 10 Hz, potentially allowing dynamic adjustment of treatment parameters. Unfortunately the absence of image data from tracking systems prevents the practice of ART, and for some patients may preclude the use of smaller margins around the prostate that would allow better sparing of nearby normal tissues. The hypothesis of the proposed research is that the prostate markers localized during treatment delivery can be used as the basis for mapping reference CT image data into the treatment space to estimate pre-treatment CT image data acceptable for calculating delivered dose. In particular the markers will be used for computing the image-match term in a Bayesian-like framework to optimize the non-rigid registration of a statistically trainable deformable shape model of a prostate, including immediately surrounding tissues, with marker positions in the treatment space. A patient-specific model, called an m-rep, created from the reference planning image for the patient being treated will embed the underlying image data, including prostate-related marker coordinates, in the model-related coordinate system unique to m- reps. The deformed m-rep created by the registration process to match the markers located during treatment implies a transformation that maps the entire reference image data to the treatment space to estimate the pre-treatment CT image. The tissue region in and around the prostate is mapped diffeomorphically. The overall aim is to establish proof of concept for estimating pre-treatment CT images acceptable for calculating delivered dose as described above PUBLIC HEALTH RELEVANCE: The hypothesis of the proposed research is that prostate markers localized during radiation therapy treatment delivery can be used as the basis for mapping reference CT image data into the treatment space to estimate pre-treatment CT image data acceptable for calculating delivered dose. In particular the markers will be used for computing the image-match term in a Bayesian-like framework to optimize the non-rigid registration of a statistically trainable deformable shape model of a prostate, including immediately surrounding tissues, with marker positions in the treatment space. A patient- specific model, called an m-rep, created from the reference planning image for the patient being treated will embed the underlying image data, including prostate-related marker coordinates, in the model-related coordinate system unique to m-reps. The deformed m-rep created by the registration process to match the markers located during treatment implies a transformation that maps the entire reference image data to the treatment space to estimate the pre-treatment CT image. The tissue region in and around the prostate is mapped diffeomorphically. The overall aim is to establish proof of concept for estimating pre-treatment CT images acceptable for calculating delivered dose as described above.

描述（由申请人提供）：在前列腺癌的放射治疗期间，普遍的做法是将前列腺定位在许多（理想情况下是所有的治疗日期），以实现向前列腺递送高剂量的目标，同时大大避开附近的放射线敏感正常组织。前列腺可以在治疗前立即将其定位于立即获得的CT图像中，或通过在治疗分娩期间植入前列腺的标记。 CT成像的优点在于，图像数据为计算辐射剂量的基础提供了根据需要定期调整治疗计划的程序，以便按照最初计划提供最终剂量分布，以定期调整治疗计划，以定期调整治疗计划。 CT成像的缺点包括货币，时空费用。标记跟踪的主要优点包括每次剂量分数期间的易用性和频繁采样，例如10 Hz，可能允许对治疗参数进行动态调整。不幸的是，从跟踪系统中没有图像数据阻止了艺术的实践，并且对于某些患者而言，可能会在前列腺周围使用较小的边缘，从而可以更好地保留附近的正常组织。拟议的研究的假设是，可以将治疗过程中定位的前列腺标记物用作将参考CT图像数据映射到治疗空间中的基础，以估算可接受计算剂量的可接受的预处理CT图像数据。特别是，这些标记将用于计算类似贝叶斯的框架中的图像匹配项，以优化前列腺的统计可训练的可变形形状模型的非刚性注册，包括立即周围的组织，在治疗空间中具有标记位置。由患者的特定模型，称为M-REP，是由接受治疗的患者的参考计划图像创建的，将嵌入基础图像数据，包括与前列腺相关的标记坐标，包括M-Reps独特的模型相关坐标系。由注册过程创建的用于匹配处理过程中的标记的变形M-REP意味着将整个参考图像数据映射到处理空间以估算预处理前CT图像的转换。前列腺内外的组织区域被绘制的映射量。总体目的是建立概念证明，以估算预处理的CT图像，如上所述，可接受的计算剂量可接受的公共卫生相关性：拟议研究的假设是，在放射治疗治疗过程中定位的前列腺标志物可以用作将CT参考数据映射到治疗空间中，以估算估算预先培养的图像CT数据可接受的基础，以计算ct图像数据可接受。特别是，这些标记将用于计算类似贝叶斯的框架中的图像匹配项，以优化前列腺的统计可训练的可变形形状模型的非刚性注册，包括立即周围的组织，在治疗空间中具有标记位置。一种称为M-REP的患者特异性模型，该模型是由接受治疗的患者的参考计划图像创建的，它将嵌入基础图像数据，包括与前列腺相关的标记坐标，在模型相关的坐标系中，是M-REPS独特的模型相关坐标系。由注册过程创建的用于匹配处理过程中的标记的变形M-REP意味着将整个参考图像数据映射到处理空间以估算预处理前CT图像的转换。前列腺内外的组织区域被绘制的映射量。总体目的是建立概念证明，以估算可接受的预处理CT图像，以计算上述剂量。