The influence of dose grid resolution on beam selection strategies in radiotherapy treatment design

Ryan Acosta (), Matthias Ehrgott (), Allen Holder (), Daniel Nevin (), Josh Reese () and Bill Salter ()
Additional contact information
Ryan Acosta: Stanford University
Matthias Ehrgott: The University of Auckland
Allen Holder: Trinity University
Daniel Nevin: Texas A&M University
Josh Reese: Trinity University
Bill Salter: University of Utah Huntsman Cancer Institute

A chapter in Optimization in Medicine, 2008, pp 1-23 from Springer

Abstract: Summary The design of a radiotherapy treatment includes the selection of beam angles (geometry problem), the computation of a fluence pattern for each selected beam angle (intensity problem), and finding a sequence of configurations of a multileaf collimator to deliver the treatment (realization problem). While many mathematical optimization models and algorithms have been proposed for the intensity problem and (to a lesser extent) the realization problem, this is not the case for the geometry problem. In clinical practice, beam directions are manually selected by a clinician and are typically based on the clinician’s experience. Solving the beam selection problem optimally is beyond the capability of current optimization algorithms and software. However, heuristic methods have been proposed. In this paper we study the influence of dose grid resolution on the performance of these heuristics for a clinical case. Dose grid resolution refers to the spatial arrangement and size of dose calculation voxels. In particular, we compare the solutions obtained by the heuristics with those achieved by a clinician using a commercial planning system. Our results show that dose grid resolution has a considerable influence on the performance of most heuristics.

Keywords: Intensity modulated radiation therapy; beam angle selection; heuristics; vector quantization; dose grid resolution; medical physics; optimization (search for similar items in EconPapers)
Date: 2008
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-0-387-73299-2_1

Ordering information: This item can be ordered from
http://www.springer.com/9780387732992

DOI: 10.1007/978-0-387-73299-2_1

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().