Integrating soft and hard dose‐volume constraints into hierarchical constrained IMRT optimization

Abstract

Purpose Dose‐volume constraints (DVCs) continue to be common features in intensity‐modulated radiation therapy (IMRT) prescriptions, but they are non‐convex and difficult to incorporate. We propose computationally efficient methods to incorporate dose‐volume constraints (DVCs) into automated IMRT planning. Methods We propose a two‐phase approach: in phase‐1, we solve a convex approximation with DVCs. Although this convex approximation does not guarantee DVC satisfaction, it provides crucial initial information about voxels likely to receive doses below DVC thresholds. Subsequently, phase‐2 solves an optimization problem with maximum dose constraints imposed on those subthreshold voxels. We further categorize DVCs into hard‐ and soft‐DVCs, where hard‐DVCs are strictly enforced by the optimization and soft‐DVCs are encouraged in the objective function. We tested this approach in our automated treatment planning system which is based on hierarchical constrained optimization. Performance is demonstrated on a series of paraspinal, lung, oligometastasis, and prostate cases as well as a small paraspinal case for which we can computationally afford to obtain a ground‐truth by solving a non‐convex optimization problem. Results The proposed algorithm successfully meets all the hard‐DVCs while increasing the overall computational time of the baseline planning process (without DVCs) by 20%, 10%, and 11% for paraspinal, oligometastasis, and prostate cases, respectively. For a soft‐DVC applied to the lung case, the dose‐volume histogram curve moves toward the desired direction and the computational time is increased by 11%. For a low‐resolution paraspinal case, the ground‐truth solution process using mixed‐integer programming methods required 15 h while the proposed algorithm converges in only 2 min with a proximal solution. Conclusions A computationally tractable algorithm to handle hard‐ and soft‐DVCs is developed which is capable of satisfying DVCs without any parameter tweaking. Although the algorithm is demonstrated in our in‐house developed automated treatment planning system, it can potentially be used in any constrained optimization framework.

Automated proton treatment planning with robust optimization using constrained hierarchical optimization

“…We subsequently identify the subthreshold voxels for each scenario and impose maximum dose constraints on them in each of the following two steps. This approach ensures the DVCs satisfaction in all the scenarios and can also be integrated in any constrained optimization framework, including nonrobust optimization 12 …”

“…More details on the DVC method can be found in Ref. [12].…”

“…Recently ECHO has been extended to also incorporate dosevolume constraints (DVCs). 12 ECHO is designed to meet all the hard maximum/mean/dose-volume constraints on the OARs and target (note, DVCs are applied to OARs only, and not the target). The algorithm proceeds sequentially, in three steps: (a) maximizes the tumor coverage, (b) minimizes OARs doses, and (c) optimizes the plan delivery.…”

Clinical Experience of Automated SBRT Paraspinal and Other Metastatic Tumor Planning With Constrained Hierarchical Optimization

“…A computationally efficient technique to incorporate dose-volume constraints into fluence map optimization has been developed and will be implemented into ECHO in the near future. 21 Automated treatment planning algorithms are currently available in multiple commercial treatment planning systems. One commercially available algorithm is based on KBP.…”

Deep Learning 3D Dose Prediction for Conventional Lung IMRT Using Consistent/Unbiased Automated Plans

“…In this work, we employ our in-house automated treatment planning system, internally referred to expedited constrained hierarchical optimization (ECHO), to generate consistent high-quality plans as an input for our DL model. ECHO generates consistent high-quality plans by solving a sequence of constrained large-scale optimization problem [28,29,30,31,32,33]. ECHO is integrated with Eclipse and is used in our daily clinical routine, with more than 4000 patients treated to date.…”