Premium
TH‐A‐116‐09: A Novel Prior‐Knowledge‐Based Optimization Algorithm for Automatic Treatment Planning and Adaptive Radiotherapy Re‐Planning
Author(s) -
Zarepisheh M,
Long T,
Li N,
Romeijn E,
Jia X,
Jiang S
Publication year - 2013
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4815738
Subject(s) - plan (archaeology) , pareto principle , computer science , radiation treatment planning , voxel , weighting , pareto optimal , algorithm , multi objective optimization , data mining , artificial intelligence , mathematical optimization , machine learning , mathematics , radiation therapy , medicine , archaeology , radiology , history
Purpose: To develop a novel algorithm that takes existing prior‐knowledge information into account in optimization to facilitate automatic treatment planning and adaptive radiotherapy (ART) re‐planning. Methods: We developed an algorithm to automatically create a treatment plan guided by the DVH curves of a reference plan that contains information on the clinician approved dose‐volume trade‐offs among different targets/organs and within the same organ. This method has applications in automatic treatment planning and ART re‐planning. In ART, the reference plan is the initial plan for the same patient, while for automatic treatment planning the reference plan is selected (based on patient similarity) from a library of clinically approved and delivered plans of previously treated patients with similar medical conditions. The proposed algorithm employs a voxel‐based optimization model and approximates the large voxel‐based Pareto surface iteratively. The voxel weights are iteratively adjusted to approach a plan that is similar to the reference plan in terms of the DVHs. If the reference plan is feasible but not Pareto optimal, the algorithm generates a Pareto optimal plan consistent with the reference DVHs. If the reference plan is too restricting, the algorithm generates a Pareto plan with DVHs close to the reference ones. Results: The algorithm was tested using a series of patient cases and found to be able to automatically adjust the voxel‐weighting factors automatically in order to generate a Pareto plan with DVHs similar to the reference plan. The algorithm has been implemented on GPU for high efficiency. Conclusion: A novel prior‐knowledge‐based optimization algorithm has been developed that uses the prior knowledge in optimization process to automatically adjust the voxel weights and generate a clinical optimal plan at high efficiency. It is found that the new algorithm can significantly improve the plan quality and planning efficiency in ART re‐planning and automatic treatment planning.