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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，通常的做法是在治疗的许多天（理想地是所有天）定位前列腺，以实现向前列腺递送高剂量同时极大地保护附近的放射敏感性正常组织的目标。前列腺可以在治疗前立即获取的CT图像中定位，或者通过在治疗递送期间植入前列腺中的跟踪标记来定位。CT成像的优点在于，图像数据提供了计算实际输送到前列腺和周围组织的辐射剂量的基础，这是自适应放射治疗（ART）所需的，是一种定期调整治疗计划的过程，以便按照最初计划输送最终剂量分布。CT成像的缺点包括金钱、空间和时间费用。标记物跟踪的主要优点包括易于使用和在每个剂量部分期间频繁采样，例如，10 Hz，可能允许动态调整治疗参数。不幸的是，缺乏来自跟踪系统的图像数据阻止了ART的实践，并且对于一些患者，可能排除在前列腺周围使用更小的边缘，这将允许更好地保留附近的正常组织。所提出的研究的假设是，在治疗递送期间定位的前列腺标记物可以用作将参考CT图像数据映射到治疗空间中的基础，以估计可用于计算递送剂量的治疗前CT图像数据。特别地，标记将用于计算类贝叶斯框架中的图像匹配项，以优化前列腺的统计学上可训练的可变形形状模型（包括紧邻的周围组织）与治疗空间中的标记位置的非刚性配准。根据正在治疗的患者的参考计划图像创建的患者特定模型（称为m-rep）将嵌入底层图像数据，包括前列腺相关标记坐标，在模型相关的坐标系中，m-reps是唯一的。变形的m-reps由配准过程创建以匹配治疗期间定位的标记的rep意味着将整个参考图像数据映射到治疗空间的变换以估计治疗前的CT图像。前列腺中和周围的组织区域被标测为形态学的。总体目标是建立用于估计治疗前CT图像的概念验证，该图像可用于计算如上所述的输送剂量。公共卫生相关性：所提出的研究的假设是，在放射治疗输送期间定位的前列腺标记物可用作将参考CT图像数据映射到治疗空间的基础，以估计治疗前CT图像数据，该图像数据可用于计算输送剂量。特别地，标记将用于计算类贝叶斯框架中的图像匹配项，以优化前列腺的统计学上可训练的可变形形状模型（包括紧邻的周围组织）与治疗空间中的标记位置的非刚性配准。根据正在治疗的患者的参考计划图像创建的患者特定模型（称为m-rep）将嵌入底层图像数据，包括前列腺相关标记坐标，在模型相关的坐标系中，m代表是唯一的。变形的m-由配准过程创建以匹配治疗期间定位的标记的rep意味着将整个参考图像数据映射到治疗空间的变换以估计治疗前的CT图像。前列腺中和周围的组织区域被标测为形态学的。总体目标是建立用于估计治疗前CT图像的概念验证，该图像可用于计算如上所述的输送剂量。