Solving the volumetric modulated arc therapy (VMAT) problem using a sequential convex programming method

• Published in: Physics in medicine & biology Vol. 66; no. 8; pp. 85004 - 85016
• Main Authors: Dursun, Pınar, Zarepisheh, Masoud, Jhanwar, Gourav, Deasy, Joseph O
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 14.04.2021
• Subjects: direct machine parameter optimization; external radiation therapy; sequential convex programming; volumetric modulated arc therapy; external radiation therapy; sequential convex programming; direct machine parameter optimization; volumetric modulated arc therapy
• ISSN: 0031-9155; 1361-6560
• DOI: 10.1088/1361-6560/abee58

The volumetric modulated arc therapy (VMAT) problem is highly non-convex and much more difficult than the fixed-field intensity modulated radiotherapy optimization problem. To solve it efficiently, we propose a sequential convex programming algorithm that solves a sequence of convex optimization problems. Beginning by optimizing the aperture weights of many (72) evenly distributed beams using the beam's eye view of the target from each direction as the initial aperture shape, the search space is constrained to allowing the leaves to move within a pre-defined step-size. A convex approximation problem is introduced and solved to optimize the leaf positions and the aperture weights within the search space. The algorithm is equipped with both local and global search strategies, whereby a global search is followed by a local search: a large step-size results in a global search with a less accurate convex approximation, followed by a small step-size local search with an accurate convex approximation. The performance of the proposed algorithm is tested on three patients with three different disease sites (paraspinal, prostate and oligometastasis). The algorithm generates VMAT plans comparable to the ideal 72-beam fluence map optimized plans (i.e. IMRT plans before leaf sequencing) in 14 iterations and 36 mins on average. The algorithm is also tested on a small down-sampled prostate case for which we could computationally afford to obtain the ground-truth by solving the non-convex mixed-integer optimization problem exactly. This general algorithm is able to produce results essentially equivalent to the ground-truth but 12 times faster. The algorithm is also scalable and can handle real clinical cases, whereas the ground-truth solution using mixed-integer optimization can only be obtained for highly down-sampled cases.