Comparison of projection- and image-based methods for proton stopping power estimation using dual energy CT. (July 2017)

• Record Type: Journal Article
• Authors: Vilches-Freixas, Gloria; Taasti, Vicki Trier; Muren, Ludvig Paul; Petersen, Jørgen Breede Baltzer; Létang, Jean Michel; Hansen, David Christoffer; Rit, Simon
• Is Part Of: Physics and imaging in radiation oncology. Volume 3(2017)
• Page Start: 28
• Page End: 36
• Publication Date: 2017-07
• Subjects: CT noise -- Dual-energy CT -- Proton stopping power ratio -- Range uncertainty -- Robustness; Radiotherapy -- Periodicals; Radiation dosimetry -- Periodicals; Cancer -- Imaging -- Periodicals; Oncology -- Periodicals; 615.842
• DOI: 10.1016/j.phro.2017.08.001
• Languages: English
• ISSNs: 2405-6316

Abstract

Abstract: Background and Purpose: Several strategies for estimating stopping power ratio (SPR) from dual-energy CT (DECT) have been proposed to improve accuracy of proton dose calculations. DECT methods can mainly be categorized into projection-based methods, where material decomposition is performed prior to image reconstruction, and image-based methods, where decomposition takes place after image reconstruction. With the advent of photon-counting and dual-layer technology, projection-based methods could be considered for SPR estimation. In this simulation-based study we compared the SPR accuracy of one projection- and three image-based DECT methods. Materials and Methods: X-ray CT projections of the female ICRP phantom were simulated using two different X-ray spectra with a realistic detector response and noise levels. ICRP slices at four different locations were selected. Reference SPR-maps were computed at 200 MeV. The SPR comparison was based on percentage deviation inside ROIs and relative range errors calculated with Radon transform of difference maps. Results: SPR root-mean-square errors (RMSE) over the selected ROIs were 0.54% for the projection-based method and 0.68%, 0.61% and 0.70% for the three image-based methods. The RMSE for the relative range errors were slightly smaller for the projection-based approach, but close to zero for all decomposition domains as positive and negative errors averaged out over the slice. Conclusions: SPR estimations with the projection-based method produced slightly better results (though not statistically significant) than the three image-based methods used in this simulation-based study, therefore, with the advent of technological developments, projection-based methods could be considered for SPR estimation if projection data is available.