Tissue decomposition from dual energy CT data for MC based dose calculation in particle therapy

• Verfasst von: Hünemohr, Nora [VerfasserIn]
• Verfasst von: Paganetti, Harald [VerfasserIn]
• Verfasst von: Greilich, Steffen [VerfasserIn]
• Verfasst von: Jäkel, Oliver [VerfasserIn]
• Verfasst von: Seco, Joao [VerfasserIn]
• Titel: Tissue decomposition from dual energy CT data for MC based dose calculation in particle therapy
• Verf.angabe: Nora Hünemohr, Harald Paganetti, Steffen Greilich, Oliver Jäkel, Joao Seco
• E-Jahr: 2014
• Jahr: 23 May 2014
• Fussnoten: Gesehen am 18.08.2020
• Titel Quelle: Enthalten in: Medical physics
• Ort Quelle: Hoboken, NJ : Wiley, 1974
• Jahr Quelle: 2014
• Band/Heft Quelle: 41(2014,6) Artikel-Nummer 061714, 14 Seiten
• ISSN Quelle: 2473-4209
• ISSN Quelle: 1522-8541
• DOI: doi:10.1118/1.4875976
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Applications
• Sach-SW: Biological material
• Sach-SW: bone
• Sach-SW: Calibrating of instruments or apparatus
• Sach-SW: calibration
• Sach-SW: Carbon
• Sach-SW: Collisional energy loss
• Sach-SW: composition
• Sach-SW: Computed tomography
• Sach-SW: Computerised tomographs
• Sach-SW: computerised tomography
• Sach-SW: Digital computing or data processing equipment or methods
• Sach-SW: Dose-volume analysis
• Sach-SW: dosimetry
• Sach-SW: Dosimetry
• Sach-SW: Dosimetry/exposure assessment
• Sach-SW: dual energy CT
• Sach-SW: e.g. blood
• Sach-SW: e.g. from bit-mapped to bit-mapped creating a similar image
• Sach-SW: Haemocytometers
• Sach-SW: Image data processing or generation
• Sach-SW: image denoising
• Sach-SW: Image enhancement or restoration
• Sach-SW: in general
• Sach-SW: Lungs
• Sach-SW: Medical image noise
• Sach-SW: medical image processing
• Sach-SW: Monte Carlo
• Sach-SW: Monte Carlo methods
• Sach-SW: Noise
• Sach-SW: Protons
• Sach-SW: radiation therapy
• Sach-SW: Radiation therapy
• Sach-SW: range uncertainty
• Sach-SW: Scintigraphy
• Sach-SW: specially adapted for specific applications
• Sach-SW: stoichiometric calibration
• Sach-SW: Testing or calibrating of apparatus or arrangements provided for in groups G01D1/00 to G01D15/00
• Sach-SW: Tissues
• Sach-SW: urine
• Sach-SW: Verification
• Sach-SW: WEPL
• K10plus-PPN: 1727331893

Abstract

Purpose: 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 ϱe and effective atomic number Zeff are calculated for 71 tabulated tissue compositions. For MC simulations, the mass density is derived via one linear fit in the ϱe that covers the entire range of tissue compositions (except lung tissue). Elemental mass fractions are predicted from the ϱe and the Zeff 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%. MeanI-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 ϱe associated with an absolute noise of ±0.01 and Zeff 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.