Beam orientation optimization in intensity‐modulated radiation treatment planning

Abstract

Beam direction optimization is an important problem in radiation therapy. In intensity modulated radiation therapy (IMRT), the difficulty for computer optimization of the beam directions arises from the fact that they are coupled with the intensity profiles of the incident beams. In order to obtain the optimal incident beam directions using iterative or stochastic methods, the beam profiles ought to be optimized after every change of beam configuration. In this paper we report an effective algorithm to optimize gantry angles for IMRT. In our calculation the gantry angles and the beam profiles (beamlet weights) were treated as two separate groups of variables. The gantry angles were sampled according to a simulated annealing algorithm. For each sampled beam configuration, beam profile calculation was done using a fast filtered backprojection (FBP) method. Simulated annealing was also used for beam profile optimization to examine the performance of the FBP for beam orientation optimization. Relative importance factors were incorporated into the objective function to control the relative importance of the target and the sensitive structures. Minimization of the objective function resulted in the best possible beam orientations and beam profiles judged by the given objective function. The algorithm was applied to several model problems and the results showed that the approach has potential for IMRT applications.

A Review of intensity modulated radiation therapy: Incorporating a report on the seventh education workshop of the ACPSEM — ACT/NSW branch

“…Since then there has been the steady growth and development of IMRT optimisation and delivery techniques 1,[25][26][27][28][29][30][31][32][33][34][35][36] . The delivery of IMRT using MLC based methods is now at the point where it is an attainable treatment option for many centres.…”

Intensity modulated radiation therapy with field rotation—a time-varying fractionation study

“…Some studies indicate that expanding the set of examined beam angles is not fruitful in improving the dose distribution [4][5][6][7][8][9], as others indicate that the mean dose sparing is improved [10]. Some approaches have applied evolutionary or genetic algorithms [11][12][13][14], simulated annealing [1,[15][16][17], particle swarm algorithms [18] and integer programming [19][20][21][22] to the problem of choosing beam angles if the total number is limited. Recent beam angle optimization approaches are proposed based on nested partitions framework [23], geometric algorithms [24], gradient-based optimization [25], and direct aperture optimization [26].…”

“…Using more beam angles lengthens the treatment session, slows machine throughput, risks the stability of patient positioning, and taxes available mechanisms for quality assurance. In investigations of beam angle selection, a goal has been to reduce the number of beams needed in treatment without compromising a dose objective [1][2][3]. Some studies indicate that expanding the set of examined beam angles is not fruitful in improving the dose distribution [4][5][6][7][8][9], as others indicate that the mean dose sparing is improved [10].…”

Fast Simultaneous Angle, Wedge, and Beam Intensity Optimization in Inverse Radiotherapy Planning

“…For the second stage one uses algorithms from linear programming (Bahr et al, 1968;Bollmann et al, 1981;Hodes, 1974;Rosen et al, 1991) or nonlinear, in particular quadratic, programming (Brahme, 1995;Censor et al, 1988;Chriss et al, 1995;Cooper, 1978;van Dalen et al, 2000;Holmes and Mackie, 1994;Hristov and Fallone, 1997;McDonald and Rubin, 1977;Redpath et al, 1976;Shu et al, 1998;Starkschall, 1984;Stein et al, 1997;de Wagter et al, 1998;Xing and Chen, 1996) and control theory (Hristov and Fallone, 1998) including multiple objective approaches (Cotrutz et al, 2001;Hamacher and Küfer, 2002;Yu, 1997). Also iterative dose reconstruction techniques (Bortfeld et al, 1990;Holmes et al, 1995;Pugachev et al, 2000;Söderström and Brahme, 1992) and methods from global optimization have been developped (Wu and Zhu, 2001b). Moreover, it must be mentioned that one-stage algorithms have been designed by means of mixed integer programming (Burkard et al, 1995;Langer et al, 1996;Lee et al, 2000) but they are time-and memory-consuming, too.…”