title: Automatic treatment plan re-optimization for adaptive radiotherapy guided with the initial plan DVHs.
abstract: Adaptive radiation therapy (ART) can reduce normal tissue toxicity and/or improve tumor control through treatment adaptations based on the current patient anatomy. Developing an efficient and effective re-planning algorithm is an important step toward the clinical realization of ART. For the re-planning process, manual trial-and-error approach to fine-tune planning parameters is time-consuming and is usually considered unpractical, especially for online ART. It is desirable to automate this step to yield a plan of acceptable quality with minimal interventions. In ART, prior information in the original plan is available, such as dose-volume histogram (DVH), which can be employed to facilitate the automatic re-planning process. The goal of this work is to develop an automatic re-planning algorithm to generate a plan with similar, or possibly better, DVH curves compared with the clinically delivered original plan. Specifically, our algorithm iterates the following two loops. An inner loop is the traditional fluence map optimization, in which we optimize a quadratic objective function penalizing the deviation of the dose received by each voxel from its prescribed or threshold dose with a set of fixed voxel weighting factors. In outer loop, the voxel weighting factors in the objective function are adjusted according to the deviation of the current DVH curves from those in the original plan. The process is repeated until the DVH curves are acceptable or maximum iteration step is reached. The whole algorithm is implemented on GPU for high efficiency. The feasibility of our algorithm has been demonstrated with three head-and-neck cancer IMRT cases, each having an initial planning CT scan and another treatment CT scan acquired in the middle of treatment course. Compared with the DVH curves in the original plan, the DVH curves in the resulting plan using our algorithm with 30 iterations are better for almost all structures. The re-optimization process takes about 30 s using our in-house optimization engine.
authors.affiliation: Department of Radiation Medicine and Applied Sciences, Center for Advanced Radiotherapy Technologies and University of California San Diego, La Jolla, CA 92037-0843, USA. Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China.
authors.forename: Nan
authors.initials: N
authors.lastname: Li
authors.forename: Masoud
authors.initials: M
authors.lastname: Zarepisheh
authors.forename: Andres
authors.initials: A
authors.lastname: Uribe-Sanchez
authors.forename: Kevin
authors.initials: K
authors.lastname: Moore
authors.forename: Zhen
authors.initials: Z
authors.lastname: Tian
authors.forename: Xin
authors.initials: X
authors.lastname: Zhen
authors.forename: Yan Jiang
authors.initials: YJ
authors.lastname: Graves
authors.forename: Quentin
authors.initials: Q
authors.lastname: Gautier
authors.forename: Loren
authors.initials: L
authors.lastname: Mell
authors.forename: Linghong
authors.initials: L
authors.lastname: Zhou
authors.forename: Xun
authors.initials: X
authors.lastname: Jia
authors.forename: Steve
authors.initials: S
authors.lastname: Jiang
mesh_terms: D000465:Algorithms; D001331:Automation; D006258:Head and Neck Neoplasms / Q000473:pathology / Q000532:radiotherapy; D006801:Humans; D009929:Organ Size; D011829:Radiation Dosage; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D050397:Radiotherapy, Intensity-Modulated / Q000379:methods*