title: Tissue decomposition from dual energy CT data for MC based dose calculation in particle therapy.
abstract: OBJECTIVE
  The authors describe a novel method of predicting mass density and elemental mass fractions of tissues from dual energy CT (DECT) data for Monte Carlo (MC) based dose planning.
  
  
  METHODS
  The relative electron density ϱ(e) and effective atomic number Z(eff) are calculated for 71 tabulated tissue compositions. For MC simulations, the mass density is derived via one linear fit in the ϱ(e) that covers the entire range of tissue compositions (except lung tissue). Elemental mass fractions are predicted from the ϱ(e) and the Z(eff) in combination. Since particle therapy dose planning and verification is especially sensitive to accurate material assignment, differences to the ground truth are further analyzed for mass density, I-value predictions, and stopping power ratios (SPR) for ions. Dose studies with monoenergetic proton and carbon ions in 12 tissues which showed the largest differences of single energy CT (SECT) to DECT are presented with respect to range uncertainties. The standard approach (SECT) and the new DECT approach are compared to reference Bragg peak positions.
  
  
  RESULTS
  Mean deviations to ground truth in mass density predictions could be reduced for soft tissue from (0.5±0.6)% (SECT) to (0.2±0.2)% with the DECT method. Maximum SPR deviations could be reduced significantly for soft tissue from 3.1% (SECT) to 0.7% (DECT) and for bone tissue from 0.8% to 0.1%. Mean I-value deviations could be reduced for soft tissue from (1.1±1.4%, SECT) to (0.4±0.3%) with the presented method. Predictions of elemental composition were improved for every element. Mean and maximum deviations from ground truth of all elemental mass fractions could be reduced by at least a half with DECT compared to SECT (except soft tissue hydrogen and nitrogen where the reduction was slightly smaller). The carbon and oxygen mass fraction predictions profit especially from the DECT information. Dose studies showed that most of the 12 selected tissues would profit significantly (up to 2.2%) from DECT material decomposition with no noise present. The ϱ(e) associated with an absolute noise of ±0.01 and Z(eff) associated with an absolute noise of ±0.2 resulted in ±10% standard variation in the carbon and oxygen mass fraction prediction.
  
  
  CONCLUSIONS
  Accurate stopping power prediction is mainly determined by the correct mass density prediction. Theoretical improvements in range predictions with DECT data in the order of 0.1%-2.1% were observed. Further work is needed to quantify the potential improvements from DECT compared to SECT in measured image data associated with artifacts and noise.
authors.affiliation: Medical Physics in Radiation Oncology, German Cancer Research Center, 69120 Heidelberg, Germany.
authors.forename: Nora
authors.initials: N
authors.lastname: Hünemohr
authors.affiliation: Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114.
authors.forename: Harald
authors.initials: H
authors.lastname: Paganetti
authors.affiliation: Medical Physics in Radiation Oncology, German Cancer Research Center, 69120 Heidelberg, Germany.
authors.forename: Steffen
authors.initials: S
authors.lastname: Greilich
authors.affiliation: Medical Physics in Radiation Oncology, German Cancer Research Center, 69120 Heidelberg, Germany and Department of Radiation Oncology and Radiation Therapy, University Hospital of Heidelberg, 69120 Heidelberg, Germany.
authors.forename: Oliver
authors.initials: O
authors.lastname: Jäkel
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; D001842:Bone and Bones / Q000528:radiation effects; D003198:Computer Simulation; D004583:Electrons; D063193:Heavy Ion Radiotherapy; D016014:Linear Models; D008168:Lung / Q000528:radiation effects; D008954:Models, Biological; D009010:Monte Carlo Method; D061766:Proton Therapy; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000379:methods*; D035501:Uncertainty; D011522:Protons
chemical_list: D011522:Protons