David Djajaputra | PubMed Health

PubMed2003

A fast dose calculation method based on table lookup for IMRT optimization.

This note describes a fast dose calculation method that can be used to speed up the optimization process in intensity-modulated radiotherapy (IMRT). Most iterative optimization algorithms in IMRT require a large number of dose calculations to achieve convergence and therefore the total amount of time needed for the IMRT planning can be substantially reduced by using a faster dose calculation method. The method that is described in this note relies on an accurate dose calculation engine that is used to calculate an approximate dose kernel for each beam used in the treatment plan. Once the kernel is computed and saved, subsequent dose calculations can be done rapidly by looking up this kernel. Inaccuracies due to the approximate nature of the kernel in this method can be reduced by performing scheduled kernel updates. This fast dose calculation method can be performed more than two orders of magnitude faster than the typical superposition/convolution methods and therefore is suitable for applications in which speed is critical, e.g., in an IMRT optimization that requires a simulated annealing optimization algorithm or in a practical IMRT beam-angle optimization system.

Algorithms↗

PubMed2003

Intensity-modulated radiotherapy optimization with gEUD-guided dose-volume objectives.

Currently, most intensity-modulated radiation therapy systems use dose-volume (DV)-based objectives. Although acceptable plans can be generated using these objectives, much trial and error is necessary to plan complex cases with many structures because numerous parameters need to be adjusted. An objective function that makes use of a generalized equivalent uniform dose (gEUD) was developed recently that has the advantage of involving simple formulae and fewer parameters. In addition, not only does the gEUD-based optimization provide the same coverage of the target, it provides significantly better protection of critical structures. However, gEUD-based optimization may not be superior once dose distributions and dose-volume histograms (DVHs) are used to evaluate the plan. Moreover, it is difficult to fine-tune the DVH with gEUD-based optimization. In this paper, we propose a method for combining the gEUD-based and DV-based optimization approaches to overcome these limitations. In this method, the gEUD optimization is performed initially to search for a solution that meets or exceeds most of the treatment objectives. Depending on the requirements, DV-based optimization with a gradient technique is then used to fine-tune the DVHs. The DV constraints are specified according to the gEUD plan, and the initial intensities are obtained from the gEUD plan as well. We demonstrated this technique in two clinical cases: aprostate cancer and ahead and neck cancer case. Compared with the DV-optimized plan, the gEUD plan provided better protection of critical structures and the target coverage was similar. However, homogeneities were slightly poorer. The gEUD plan was then fine-tuned with DV constraints, and the resulting plan was superior to the other plans in terms of the dose distributions. The planning time was significantly reduced as well. This technique is an effective means of optimizing individualized treatment plans.

Adenocarcinoma↗