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

Aperture-based inverse planning for intensity modulated radiation therapy 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 closer 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.