Mao Y, Jia X, Zarepisheh M, Jiang S
University of Minnesota, Minneapolis, Minnesota.
University of California, San Diego, La Jolla, CA.
Med Phys. 2012 Jun;39(6Part19):3847. doi: 10.1118/1.4735701.
Dose-volume histogram (DVH) is a clinically relevant criterion to evaluate a treatment plan quality. It is hence desirable to incorporate DVH constraints in treatment planning process for intensity modulated radiation therapy (IMRT). Yet, these constraints usually lead to difficulties due to their non-convex nature. The purpose of this project is to solve the IMRT optimization problem with DVH constraints using a hybrid-input-output (HIO) method.
The IMRT optimization problem finds a dose distribution z under two constraints, where the elements of the vector z are the dose value at each spatial coordinate. First, there exist a corresponding fluence map x such that Dx=z and x>0, where D is the dose deposition matrix. Second, the dose distribution z should satisfy the DVH constraints. These two constraints define two sets of the dose distributions, denoted by A and B, and the solution to the IMRT problem lies in the intersection of these two sets. Our method finds the solution via the HIO algorithm that iteratively updates the solution according to its projections onto the two sets until convergence. The projection to A is handled by solving a least square problem and the projection to B is achieved by gradually adjusting voxel doses that validate the DVH criteria to meet the constraints.
We have tested our algorithm using 7-field IMRT plans for 4 prostate cancer cases. Clinically relevant DVH constraints are considered for PTV, rectum, and bladder. In all the cases, the algorithm is able to find the solutions that satisfy all the DVH constraints.
We have developed an algorithm to solve the IMRT optimization problem with DVH constraints using the HIO approach. Tests conducted in prostate cancer cases have demonstrated the effectiveness of our algorithm.
剂量体积直方图（DVH）是评估治疗计划质量的临床相关标准。因此，在调强放射治疗（IMRT）的治疗计划过程中纳入DVH约束是很有必要的。然而，由于这些约束的非凸性，通常会导致困难。本项目的目的是使用混合输入输出（HIO）方法解决具有DVH约束的IMRT优化问题。
IMRT优化问题是在两个约束条件下找到剂量分布z，其中向量z的元素是每个空间坐标处的剂量值。首先，存在一个相应的通量图x，使得Dx = z且x > 0，其中D是剂量沉积矩阵。其次，剂量分布z应满足DVH约束。这两个约束定义了两组剂量分布，分别表示为A和B，IMRT问题的解位于这两组的交集处。我们的方法通过HIO算法找到解，该算法根据其在两组上的投影迭代更新解，直到收敛。对A的投影通过求解最小二乘问题来处理，对B的投影通过逐步调整验证DVH标准以满足约束的体素剂量来实现。
我们使用4例前列腺癌病例的7野IMRT计划测试了我们的算法。考虑了针对计划靶区（PTV）、直肠和膀胱的临床相关DVH约束。在所有病例中，该算法都能够找到满足所有DVH约束的解。
我们开发了一种使用HIO方法解决具有DVH约束的IMRT优化问题的算法。在前列腺癌病例中进行的测试证明了我们算法的有效性。
