Comparative behaviour of the Dynamically Penalized Likelihood algorithm in inverse radiation therapy planning

Jorge Llacer, Timothy D. Solberg, Claus Promberger

Research output: Contribution to journalArticle

50 Scopus citations

Abstract

This paper presents a description of tests carried out to compare the behaviour of five algorithms in inverse radiation therapy planning: (1) The Dynamically Penalized Likelihood (DPL), an algorithm based on statistical estimation theory; (2) an accelerated version of the same algorithm; (3) a new fast adaptive simulated annealing (ASA) algorithm; (4) a conjugate gradient method; and (5) a Newton gradient method. A three-dimensional mathematical phantom and two clinical cases have been studied in detail. The phantom consisted of a U-shaped tumour with a partially enclosed 'spinal cord'. The clinical examples were a cavernous sinus meningioma and a prostate case. The algorithms have been tested in carefully selected and controlled conditions so as to ensure fairness in the assessment of results. It has been found that all five methods can yield relatively similar optimizations, except when a very demanding optimization is carried out. For the easier cases, the differences are principally in robustness, ease of use and optimization speed. In the more demanding case, there are significant differences in the resulting dose distributions. The accelerated DPL emerges as possibly the algorithm of choice for clinical practice. An appendix describes the differences in behaviour between the new ASA method and the one based on a patent by the Nomos Corporation.

Original languageEnglish (US)
Pages (from-to)2637-2663
Number of pages27
JournalPhysics in medicine and biology
Volume46
Issue number10
DOIs
StatePublished - Oct 31 2001

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging

Fingerprint Dive into the research topics of 'Comparative behaviour of the Dynamically Penalized Likelihood algorithm in inverse radiation therapy planning'. Together they form a unique fingerprint.

  • Cite this