Tissue segmentation in Monte Carlo treatment planning: A simulation study using dual-energy CT images

PurposeTissue segmentation is an important step in Monte Carlo (MC) dose calculation and is often done uncritically. A new approach to tissue segmentation using dual-energy CT images is studied in this work.Materials and methodsA simple MC model of a CT scanner was built and CT images of phantoms with ten tissue-equivalent cylinders were simulated using soft and hard X-ray spectra. The Z and ρe of the cylinders were extracted using a formalism based on a parameterization of the linear attenuation coefficient.ResultsIt was shown that in order to extract Z and ρe with a reasonable accuracy, hard X-ray beams have to be used for scanning. When an additional filtration of 9 mm of aluminium in the CT X-ray beam is used, beam hardening in high density materials is suppressed and the mean errors of the extraction of Z and ρe for 10 tissue-equivalent materials in a small tissue-equivalent phantom are 3.7% and 3.1%, respectively.ConclusionsMC simulations were used to show that the extraction of Z and ρe for a number of tissue-equivalent materials using dual-energy CT images is possible which improves tissue segmentation for Monte Carlo dose calculations, as demonstrated with a 250 kVp photon beam dose calculation.