Sparsity constrained split feasibility for dose-volume constraints in inverse planning of intensity-modulated photon or proton therapy. (5th April 2017)

• Record Type: Journal Article
• Authors: Penfold, Scott; Zalas, Rafał; Casiraghi, Margherita; Brooke, Mark; Censor, Yair; Schulte, Reinhard
• Is Part Of: Physics in medicine & biology. Volume 62:Number 9(2017:May)
• Page Start: 3599
• Page End: 3618
• Publication Date: 2017-04-05
• Subjects: dose-volume constraints -- intensity-modulated proton therapy -- sparsity constraints -- split feasibility -- inverse planning -- automatic relaxation method -- CQ-algorithm; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/aa602b
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: A split feasibility formulation for the inverse problem of intensity-modulated radiation therapy treatment planning with dose-volume constraints included in the planning algorithm is presented. It involves a new type of sparsity constraint that enables the inclusion of a percentage-violation constraint in the model problem and its handling by continuous (as opposed to integer) methods. We propose an iterative algorithmic framework for solving such a problem by applying the feasibility-seeking CQ -algorithm of Byrne combined with the automatic relaxation method that uses cyclic projections. Detailed implementation instructions are furnished. Functionality of the algorithm was demonstrated through the creation of an intensity-modulated proton therapy plan for a simple 2D C-shaped geometry and also for a realistic base-of-skull chordoma treatment site. Monte Carlo simulations of proton pencil beams of varying energy were conducted to obtain dose distributions for the 2D test case. A research release of the Pinnacle 3 proton treatment planning system was used to extract pencil beam doses for a clinical base-of-skull chordoma case. In both cases the beamlet doses were calculated to satisfy dose-volume constraints according to our new algorithm. Examination of the dose-volume histograms following inverse planning with our algorithm demonstrated that it performed as intended. The application of our proposed algorithm to dose-volume constraint inverse planning was successfully demonstrated. Comparison with optimized dose distributions from the research release of the Pinnacle 3 treatment planning system showed the algorithm could achieve equivalent or superior results.