12,869 research outputs found
A Dynamic Programming Approach to Adaptive Fractionation
We conduct a theoretical study of various solution methods for the adaptive
fractionation problem. The two messages of this paper are: (i) dynamic
programming (DP) is a useful framework for adaptive radiation therapy,
particularly adaptive fractionation, because it allows us to assess how close
to optimal different methods are, and (ii) heuristic methods proposed in this
paper are near-optimal, and therefore, can be used to evaluate the best
possible benefit of using an adaptive fraction size.
The essence of adaptive fractionation is to increase the fraction size when
the tumor and organ-at-risk (OAR) are far apart (a "favorable" anatomy) and to
decrease the fraction size when they are close together. Given that a fixed
prescribed dose must be delivered to the tumor over the course of the
treatment, such an approach results in a lower cumulative dose to the OAR when
compared to that resulting from standard fractionation. We first establish a
benchmark by using the DP algorithm to solve the problem exactly. In this case,
we characterize the structure of an optimal policy, which provides guidance for
our choice of heuristics. We develop two intuitive, numerically near-optimal
heuristic policies, which could be used for more complex, high-dimensional
problems. Furthermore, one of the heuristics requires only a statistic of the
motion probability distribution, making it a reasonable method for use in a
realistic setting. Numerically, we find that the amount of decrease in dose to
the OAR can vary significantly (5 - 85%) depending on the amount of motion in
the anatomy, the number of fractions, and the range of fraction sizes allowed.
In general, the decrease in dose to the OAR is more pronounced when: (i) we
have a high probability of large tumor-OAR distances, (ii) we use many
fractions (as in a hyper-fractionated setting), and (iii) we allow large daily
fraction size deviations.Comment: 17 pages, 4 figures, 1 tabl
GPU-based ultra-fast direct aperture optimization for online adaptive radiation therapy
Online adaptive radiation therapy (ART) has great promise to significantly
reduce normal tissue toxicity and/or improve tumor control through real-time
treatment adaptations based on the current patient anatomy. However, the major
technical obstacle for clinical realization of online ART, namely the inability
to achieve real-time efficiency in treatment re-planning, has yet to be solved.
To overcome this challenge, this paper presents our work on the implementation
of an intensity modulated radiation therapy (IMRT) direct aperture optimization
(DAO) algorithm on graphics processing unit (GPU) based on our previous work on
CPU. We formulate the DAO problem as a large-scale convex programming problem,
and use an exact method called column generation approach to deal with its
extremely large dimensionality on GPU. Five 9-field prostate and five 5-field
head-and-neck IMRT clinical cases with 5\times5 mm2 beamlet size and
2.5\times2.5\times2.5 mm3 voxel size were used to evaluate our algorithm on
GPU. It takes only 0.7~2.5 seconds for our implementation to generate optimal
treatment plans using 50 MLC apertures on an NVIDIA Tesla C1060 GPU card. Our
work has therefore solved a major problem in developing ultra-fast
(re-)planning technologies for online ART
Explicit optimization of plan quality measures in intensity-modulated radiation therapy treatment planning
Conventional planning objectives in optimization of intensity-modulated
radiotherapy treatment (IMRT) plans are designed to minimize the violation of
dose-volume histogram (DVH) thresholds using penalty functions. Although
successful in guiding the DVH curve towards these thresholds, conventional
planning objectives offer limited control of the individual points on the DVH
curve (doses-at-volume) used to evaluate plan quality. In this study, we
abandon the usual penalty-function framework and propose planning objectives
that more explicitly relate to DVH statistics. The proposed planning objectives
are based on mean-tail-dose, resulting in convex optimization. We also
demonstrate how to adapt a standard optimization method to the proposed
formulation in order to obtain a substantial reduction in computational cost.
We investigate the potential of the proposed planning objectives as tools for
optimizing DVH statistics through juxtaposition with the conventional planning
objectives on two patient cases. Sets of treatment plans with differently
balanced planning objectives are generated using either the proposed or the
conventional approach. Dominance in the sense of better distributed
doses-at-volume is observed in plans optimized within the proposed framework,
indicating that the DVH statistics are better optimized and more efficiently
balanced using the proposed planning objectives
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