A DVH‐guided IMRT optimization algorithm for automatic treatment planning and adaptive radiotherapy replanning. Issue 6 (15th May 2014)

• Record Type: Journal Article
• Authors: Zarepisheh, Masoud; Long, Troy; Li, Nan; Tian, Zhen; Romeijn, H. Edwin; Jia, Xun; Jiang, Steve B.
• Is Part Of: Medical physics. Volume 41:Issue 6(2014)Part 1
• Page Start: n/a
• Page End: n/a
• Publication Date: 2014-05-15
• Subjects: Therapeutic applications, including brachytherapy -- Dose‐volume analysis -- Dosimetry/exposure assessment -- Numerical optimization; biological organs -- dosimetry -- graphics processing units -- Pareto optimisation -- radiation therapy; automatic treatment planning -- adaptive radiotherapy re‐planning -- intensity modulation -- optimization -- Pareto surface; Radiation therapy -- Processor architectures; Processor configuration, e.g. pipelining -- Scintigraphy; Anatomy -- Dosimetry -- Medical treatment planning -- Adaptive radiation therapy -- Numerical modeling -- Radiation treatment -- Cancer -- Intensity modulated radiation therapy; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4875700
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: To develop a novel algorithm that incorporates prior treatment knowledge into intensity modulated radiation therapy optimization to facilitate automatic treatment planning and adaptive radiotherapy (ART) replanning. Methods: The algorithm automatically creates 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 among different portions of a DVH curve for an organ. In ART, the reference plan is the initial plan for the same patient, while for automatic treatment planning the reference plan is selected from a library of clinically approved and delivered plans of previously treated patients with similar medical conditions and geometry. The proposed algorithm employs a voxel‐based optimization model and navigates the large voxel‐based Pareto surface. 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 with the DVHs better than the reference ones. If the reference plan is too restricting for the new geometry, the algorithm generates a Pareto plan with DVHs close to the reference ones. In both cases, the new plans have similar DVH trade‐offs as the reference plans. Results: The algorithm was tested using three patient cases and found to be able to automatically adjust the voxel‐weighting factors in order to generate a Pareto plan with similar DVH trade‐offs as the reference plan. The algorithm has also been implemented on a GPU for high efficiency. Conclusions: A novel prior‐knowledge‐based optimization algorithm has been developed that 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 replanning and automatic treatment planning.