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The least-intensity feasible solution for aperture-based inverse planning in radiation therapy.

Ying Xiao (ying.xiao***at***mail.tju.edu)
Yair Censor (yair***at***math.haifa.ac.il)
Darek Michalski (darek.michalski***at***mail.tju.edu)
James Galvin (James.Galvin***at***mail.tju.edu)

Abstract: Aperture-based inverse planning (ABIP) for intensity modulated radiation therapy (IMRT) treatment planning starts with external radiation fields (beams) that fully conform to the target(s) and then superimposes sub-fields called segments to achieve complex shaping of 3D dose distributions. The segments' intensities are determined by solving a feasibility problem. The least-intensity feasible (LIF) solution, proposed and studied here, seeks a feasible solution closest to the origin, thus being of least intensity or least energy. We present a new iterative, primal-dual, algorithm for finding the LIF solution and explain our experimental observation that Cimmino's algorithm for feasibility actually converges to a close approximation of the LIF solution. Comparison with linear programming shows that Cimmino's algorithm has the additional advantage of generating much smoother solutions.

Keywords: Least-intensity, feasible solution, aperture-based inverse planning, intensity modulated radiation therapy, projection algorithms, Cimmino's algorithm.

Category 1: Applications -- Science and Engineering (Biomedical Applications )

Category 2: Convex and Nonsmooth Optimization (Convex Optimization )

Category 3: Nonlinear Optimization (Nonlinear Systems and Least-Squares )

Citation: Annals of Operations Research, Vol. 119 (2003), pp. 183-203.


Entry Submitted: 03/28/2002
Entry Accepted: 03/28/2002
Entry Last Modified: 06/16/2003

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