title: The impact of low-Z and high-Z metal implants in IMRT: a Monte Carlo study of dose inaccuracies in commercial dose algorithms.
abstract: OBJECTIVE
  The aim of the study was to evaluate the dosimetric impact of low-Z and high-Z metallic implants on IMRT plans.
  
  
  METHODS
  Computed tomography (CT) scans of three patients were analyzed to study effects due to the presence of Titanium (low-Z), Platinum and Gold (high-Z) inserts. To eliminate artifacts in CT images, a sinogram-based metal artifact reduction algorithm was applied. IMRT dose calculations were performed on both the uncorrected and corrected images using a commercial planning system (convolution/superposition algorithm) and an in-house Monte Carlo platform. Dose differences between uncorrected and corrected datasets were computed and analyzed using gamma index (Pγ<1) and setting 2 mm and 2% as distance to agreement and dose difference criteria, respectively. Beam specific depth dose profiles across the metal were also examined.
  
  
  RESULTS
  Dose discrepancies between corrected and uncorrected datasets were not significant for low-Z material. High-Z materials caused under-dosage of 20%-25% in the region surrounding the metal and over dosage of 10%-15% downstream of the hardware. Gamma index test yielded Pγ<1>99% for all low-Z cases; while for high-Z cases it returned 91% < Pγ<1< 99%. Analysis of the depth dose curve of a single beam for low-Z cases revealed that, although the dose attenuation is altered inside the metal, it does not differ downstream of the insert. However, for high-Z metal implants the dose is increased up to 10%-12% around the insert. In addition, Monte Carlo method was more sensitive to the presence of metal inserts than superposition/convolution algorithm.
  
  
  CONCLUSIONS
  The reduction in terms of dose of metal artifacts in CT images is relevant for high-Z implants. In this case, dose distribution should be calculated using Monte Carlo algorithms, given their superior accuracy in dose modeling in and around the metal. In addition, the knowledge of the composition of metal inserts improves the accuracy of the Monte Carlo dose calculation significantly.
authors.affiliation: Department of Experimental and Clinical Medicine, University of Magna Graecia, Catanzaro 88100, Italy and Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114.
authors.forename: Maria Francesca
authors.initials: MF
authors.lastname: Spadea
authors.affiliation: Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114.
authors.forename: Joost Mathias
authors.initials: JM
authors.lastname: Verburg
authors.affiliation: Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano University, Milano 20133, Italy and Bioengineering Unit, Fondazione CNAO, Pavia 27100, Italy.
authors.forename: Guido
authors.initials: G
authors.lastname: Baroni
authors.affiliation: Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114.
authors.forename: Joao
authors.initials: J
authors.lastname: Seco
mesh_terms: D016477:Artifacts; D006801:Humans; D008670:Metals; D009010:Monte Carlo Method; D019736:Prostheses and Implants; D011829:Radiation Dosage; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D050397:Radiotherapy, Intensity-Modulated / Q000379:methods*
chemical_list: D008670:Metals