Department of Radiation Oncology, Mayo Clinic, Rochester, MN 55905, USA.
Med Phys. 2013 Jan;40(1):011717. doi: 10.1118/1.4769111.
This paper outlines and demonstrates a programmatic method to incorporate spatial information into a dose volume histogram (DVH) by adding vector data on the location of pixels in the dose array relative to structures in the plan to construct a vectorized dose distribution (VDD). With this data the DVH can be subgrouped according to a wide array of vector constraint sets, defining the spatial relationship of pixels to one or several structures to construct a vectorized DVH (VDVH) to reveal vector relationships of dose regions to structures.
Mathematical models for construction of the VDD and VDVH are described and a dose-vector-histogram (DVctH) is introduced as a means of specifying the location of dose features such as "hot spots." Practical detail on a programmatic approach to implement the methods is provided. A set of tests utilizing phantom and SBRT lung image sets were carried out to demonstrate ability of VDVH and DVctH to reveal clinically relevant spatial detail in dose distributions.
The VDVH and DVctH enabled decomposing DVH curves to reveal the relative location of pixels contributing to dose points on the curve. The metrics enabled specificity in defining the location and magnitude of dose features relevant to treatment plan evaluation. The VDD, VDVH, and DVctH differ from other methods described in the literature as a result of using vector based constraints for each pixel, rather than focusing only on distance by construction of a set of shells on around or within a structure and then subgrouping pixels in the overlap region.
The method is an effective means to combine spatial information with DVH metrics and provides a practical means of specifying the location of dose features with respect other structures in the treatment plan.
本文概述并演示了一种通过在剂量数组中像素的位置相对于计划中的结构添加矢量数据，将空间信息纳入剂量体积直方图（DVH）的编程方法，以构建矢量化剂量分布（VDD）。通过这些数据，DVH 可以根据广泛的矢量约束集进行分组，定义像素与一个或多个结构之间的空间关系，构建矢量化 DVH（VDVH）以揭示剂量区域与结构之间的矢量关系。
描述了构建 VDD 和 VDVH 的数学模型，并引入了剂量-矢量-直方图（DVctH），作为指定剂量特征（如“热点”）位置的一种手段。提供了一种实现该方法的编程方法的实用细节。利用体模和 SBRT 肺图像集进行了一组测试，以证明 VDVH 和 DVctH 能够揭示剂量分布中与临床相关的空间细节的能力。
VDVH 和 DVctH 使我们能够分解 DVH 曲线，以揭示对曲线上剂量点有贡献的像素的相对位置。这些指标能够在定义与治疗计划评估相关的剂量特征的位置和幅度方面具有特异性。VDD、VDVH 和 DVctH 与文献中描述的其他方法不同，因为它们为每个像素使用基于矢量的约束，而不是仅通过围绕或在结构内构建一组外壳并然后在重叠区域中分组像素来关注距离。
该方法是将空间信息与 DVH 指标结合的有效手段，并为指定治疗计划中其他结构的剂量特征位置提供了实用手段。
