Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, 32611-6595, USA.
Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, 94305-5847, USA.
Med Phys. 2018 Jul;45(7):3321-3329. doi: 10.1002/mp.12977. Epub 2018 Jun 1.
Inverse planning involves iterative optimization of a large number of parameters and is known to be a labor-intensive procedure. To reduce the scale of computation and improve characterization of isodose plan, this paper presents an isodose feature-preserving voxelization (IFPV) framework for radiation therapy applications and demonstrates an implementation of inverse planning in the IFPV domain.
A dose distribution in IFPV scheme is characterized by partitioning the voxels into subgroups according to their geometric and dosimetric values. Computationally, the isodose feature-preserving (IFP) clustering combines the conventional voxels that are spatially and dosimetrically close into physically meaningful clusters. A K-means algorithm and support vector machine (SVM) runs sequentially to group the voxels into IFP clusters. The former generates initial clusters according to the geometric and dosimetric information of the voxels and SVM is invoked to improve the connectivity of the IFP clusters. To illustrate the utility of the formalism, an inverse planning framework in the IFPV domain is implemented, and the resultant plans of three prostate IMRT and one head-and-neck cases are compared quantitatively with that obtained using conventional inverse planning technique.
The IFPV generates models with significant dimensionality reduction without compromising the spatial resolution seen in traditional downsampling schemes. The implementation of inverse planning in IFPV domain is demonstrated. In addition to the improved computational efficiency, it is found that, for the cases studied here, the IFPV-domain inverse planning yields better treatment plans than that of DVH-based planning, primarily because of more effective use of both geometric and dose information of the system during plan optimization.
The proposed IFPV provides a low parametric representation of isodose plan without compromising the essential characteristics of the plan, thus providing a practically valuable framework for various applications in radiation therapy.
逆向计划涉及对大量参数的迭代优化，众所周知，这是一项劳动密集型的工作。为了降低计算规模并提高等剂量图计划的特征描述能力，本文提出了一种用于放射治疗应用的等剂量特征保持体素化（IFPV）框架，并展示了在 IFPV 域中进行逆向计划的实现。
在 IFPV 方案中，剂量分布通过根据其几何和剂量值将体素划分为子组来进行特征描述。在计算上，等剂量特征保持（IFP）聚类将空间和剂量上接近的常规体素组合成具有物理意义的聚类。K-均值算法和支持向量机（SVM）顺序运行以将体素分组为 IFP 聚类。前者根据体素的几何和剂量信息生成初始聚类，而 SVM 则用于提高 IFP 聚类的连通性。为了说明形式主义的实用性，在 IFPV 域中实现了逆向计划框架，并定量比较了三个前列腺调强放射治疗和一个头颈部病例的结果计划，与使用传统逆向计划技术获得的结果计划进行了比较。
IFPV 在不影响传统下采样方案中看到的空间分辨率的情况下，生成具有显著降维的模型。展示了在 IFPV 域中实现逆向计划的过程。除了提高计算效率外，还发现，对于这里研究的病例，IFPV 域中的逆向计划产生的治疗计划比基于 DVH 的计划更好，主要是因为在计划优化过程中更有效地利用了系统的几何和剂量信息。
所提出的 IFPV 提供了等剂量图计划的低参数表示，而不会影响计划的基本特征，从而为放射治疗中的各种应用提供了一个实用的框架。
