A moment-based approach for DVH-guided radiotherapy treatment plan optimization

Abstract

The dose-volume histogram (DVH) is a clinically relevant criterion to evaluate the quality of a treatment plan. It is hence desirable to incorporate DVH constraints into treatment plan optimization for intensity modulated radiation therapy. Yet, the direct inclusion of the DVH constraints into a treatment plan optimization model typically leads to great computational difficulties due to the non-convex nature of these constraints. To overcome this critical limitation, we propose a new convex-moment-based optimization approach. Our main idea is to replace the non-convex DVH constraints by a set of convex moment constraints. In turn, the proposed approach is able to generate a Pareto-optimal plan whose DVHs are close to, or if possible even outperform, the desired DVHs. In particular, our experiment on a prostate cancer patient case demonstrates the effectiveness of this approach by employing two and three moment formulations to approximate the desired DVHs.

A DVH-guided IMRT optimization algorithm for automatic treatment planning and adaptive radiotherapy replanning

“…[21][22][23][24][25][26][27][28][29][30] In this paper, we aim to develop an optimization model to handle the entire DVH curves rather than a few points on the curves. The goal is to minimize an appropriately defined metric designed to represent the distance between a plan's DVHs and the reference DVHs.…”

A GPU‐accelerated and Monte Carlo‐based intensity modulated proton therapy optimization system

“…Others attempt to solve the DVH constrained optimization problems with limited successes. [21][22][23][24][25][26][27][28] This work utilizes a method that is modified from Lomax, 19 and focuses on a fast way to optimize a simple cost function, and adaptively change the cost function according to DVHs to achieve plans satisfying DVH constraints. The algorithm is also suitable for a large number of spots (on the order of 100 000).…”

Automated Radiotherapy Treatment Planning Using Fuzzy Inference Systems

“…One very interesting feature of this algorithm is that it is capable of dealing with dose-volume restrictions that are usually considered as being very difficult to include in FMO problems, namely the constraints guaranteeing that at most a given volume of the structure receives more than a given dose. Some authors have developed models using dose-volume histogram constraints [24][25][26], but these constraints have the drawback of creating a non-convex feasibility space, with many local minima. It can also be useful to consider the mean-tail-dose rather than conventional dose-volume constraints [27] (mean dose of either a hottest or coldest specified fractional volume).…”