An efficient numerical tool for dose deposition prediction applied to synchrotron medical imaging and radiation therapy. (12th July 2013)

• Record Type: Journal Article
• Authors: Mittone, Alberto; Baldacci, Fabien; Bravin, Alberto; Brun, Emmanuel; Delaire, François; Ferrero, Claudio; Gasilov, Sergei; Freud, Nicolas; Létang, Jean Michel; Sarrut, David; Smekens, François; Coan, Paola
• Is Part Of: Journal of synchrotron radiation. Volume 20:Part 5(2013)
• Page Start: 785
• Page End: 792
• Publication Date: 2013-07-12
• Subjects: Synchrotron radiation -- Periodicals; Free electron lasers -- Periodicals; 539.73505
• DOI: 10.1107/S0909049513017184
• Languages: English
• ISSNs: 0909-0495

Abstract

<abstract abstract-type="main" xml:lang="en"> <title> <x xml:space="preserve">Abstract</x> </title> <p>Medical imaging and radiation therapy are widely used synchrotron‐based techniques which have one thing in common: a significant dose delivery to typically biological samples. Among the ways to provide the experimenters with image guidance techniques indicating optimization strategies, Monte Carlo simulation has become the gold standard for accurately predicting radiation dose levels under specific irradiation conditions. A highly important hampering factor of this method is, however, its slow statistical convergence. A track length estimator (TLE) module has been coded and implemented for the first time in the open‐source Monte Carlo code <italic>GATE/Geant4</italic>. Results obtained with the module and the procedures used to validate them are presented. A database of energy‐absorption coefficients was also generated, which is used by the TLE calculations and is now also included in <italic>GATE/Geant4</italic>. The validation was carried out by comparing the TLE‐simulated doses with experimental data in a synchrotron radiation computed tomography experiment. The TLE technique shows good agreement <italic>versus</italic> both experimental measurements and the results of a classical Monte Carlo simulation. Compared with the latter, it is possible to reach a pre‐defined statistical uncertainty in about two to three orders of magnitude less time for complex geometries without loss of accuracy.</p> </abstract>