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Title: TU-FG-BRB-03: Basis Vector Model Based Method for Proton Stopping Power Estimation From Experimental Dual Energy CT Data

Abstract

Purpose: This work aims at reducing the uncertainty in proton stopping power (SP) estimation by a novel combination of a linear, separable basis vector model (BVM) for stopping power calculation (Med Phys 43:600) and a statistical, model-based dual-energy CT (DECT) image reconstruction algorithm (TMI 35:685). The method was applied to experimental data. Methods: BVM assumes the photon attenuation coefficients, electron densities, and mean excitation energies (I-values) of unknown materials can be approximated by a combination of the corresponding quantities of two reference materials. The DECT projection data for a phantom with 5 different known materials was collected on a Philips Brilliance scanner using two scans at 90 kVp and 140 kVp. The line integral alternating minimization (LIAM) algorithm was used to recover the two BVM coefficient images using the measured source spectra. The proton stopping powers are then estimated from the Bethe-Bloch equation using electron densities and I-values derived from the BVM coefficients. The proton stopping powers and proton ranges for the phantom materials estimated via our BVM based DECT method are compared to ICRU reference values and a post-processing DECT analysis (Yang PMB 55:1343) applied to vendorreconstructed images using the Torikoshi parametric fit model (tPFM). Results: For the phantommore » materials, the average stopping power estimations for 175 MeV protons derived from our method are within 1% of the ICRU reference values (except for Teflon with a 1.48% error), with an average standard deviation of 0.46% over pixels. The resultant proton ranges agree with the reference values within 2 mm. Conclusion: Our principled DECT iterative reconstruction algorithm, incorporating optimal beam hardening and scatter corrections, in conjunction with a simple linear BVM model, achieves more accurate and robust proton stopping power maps than the post-processing, nonlinear tPFM based DECT analysis applied to conventional reconstructions of low and high energy scans. Funding Support: NIH R01CA 75371; NCI grant R01 CA 149305.« less

Authors:
; ;  [1]; ; ;  [2];  [3]
  1. Washington University in St. Louis, St. Louis, MO (United States)
  2. Virginia Commonwealth University, Richmond, VA (United States)
  3. University of Pittsburgh, Pittsburgh, PA (United States)
Publication Date:
OSTI Identifier:
22653996
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 61 RADIATION PROTECTION AND DOSIMETRY; ALGORITHMS; COMPUTERIZED TOMOGRAPHY; ELECTRON DENSITY; EXPERIMENTAL DATA; IMAGE PROCESSING; ITERATIVE METHODS; MEV RANGE 100-1000; PHANTOMS; PROTONS; STOPPING POWER

Citation Formats

Zhang, S, Politte, D, O’Sullivan, J, Han, D, Porras-Chaverri, M, Williamson, J, and Whiting, B. TU-FG-BRB-03: Basis Vector Model Based Method for Proton Stopping Power Estimation From Experimental Dual Energy CT Data. United States: N. p., 2016. Web. doi:10.1118/1.4957543.
Zhang, S, Politte, D, O’Sullivan, J, Han, D, Porras-Chaverri, M, Williamson, J, & Whiting, B. TU-FG-BRB-03: Basis Vector Model Based Method for Proton Stopping Power Estimation From Experimental Dual Energy CT Data. United States. doi:10.1118/1.4957543.
Zhang, S, Politte, D, O’Sullivan, J, Han, D, Porras-Chaverri, M, Williamson, J, and Whiting, B. 2016. "TU-FG-BRB-03: Basis Vector Model Based Method for Proton Stopping Power Estimation From Experimental Dual Energy CT Data". United States. doi:10.1118/1.4957543.
@article{osti_22653996,
title = {TU-FG-BRB-03: Basis Vector Model Based Method for Proton Stopping Power Estimation From Experimental Dual Energy CT Data},
author = {Zhang, S and Politte, D and O’Sullivan, J and Han, D and Porras-Chaverri, M and Williamson, J and Whiting, B},
abstractNote = {Purpose: This work aims at reducing the uncertainty in proton stopping power (SP) estimation by a novel combination of a linear, separable basis vector model (BVM) for stopping power calculation (Med Phys 43:600) and a statistical, model-based dual-energy CT (DECT) image reconstruction algorithm (TMI 35:685). The method was applied to experimental data. Methods: BVM assumes the photon attenuation coefficients, electron densities, and mean excitation energies (I-values) of unknown materials can be approximated by a combination of the corresponding quantities of two reference materials. The DECT projection data for a phantom with 5 different known materials was collected on a Philips Brilliance scanner using two scans at 90 kVp and 140 kVp. The line integral alternating minimization (LIAM) algorithm was used to recover the two BVM coefficient images using the measured source spectra. The proton stopping powers are then estimated from the Bethe-Bloch equation using electron densities and I-values derived from the BVM coefficients. The proton stopping powers and proton ranges for the phantom materials estimated via our BVM based DECT method are compared to ICRU reference values and a post-processing DECT analysis (Yang PMB 55:1343) applied to vendorreconstructed images using the Torikoshi parametric fit model (tPFM). Results: For the phantom materials, the average stopping power estimations for 175 MeV protons derived from our method are within 1% of the ICRU reference values (except for Teflon with a 1.48% error), with an average standard deviation of 0.46% over pixels. The resultant proton ranges agree with the reference values within 2 mm. Conclusion: Our principled DECT iterative reconstruction algorithm, incorporating optimal beam hardening and scatter corrections, in conjunction with a simple linear BVM model, achieves more accurate and robust proton stopping power maps than the post-processing, nonlinear tPFM based DECT analysis applied to conventional reconstructions of low and high energy scans. Funding Support: NIH R01CA 75371; NCI grant R01 CA 149305.},
doi = {10.1118/1.4957543},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = 2016,
month = 6
}
  • Purpose: To evaluate the accuracy and robustness of a simple, linear, separable, two-parameter model (basis vector model, BVM) in mapping proton stopping powers via dual energy computed tomography (DECT) imaging. Methods: The BVM assumes that photon cross sections (attenuation coefficients) of unknown materials are linear combinations of the corresponding radiological quantities of dissimilar basis substances (i.e., polystyrene, CaCl{sub 2} aqueous solution, and water). The authors have extended this approach to the estimation of electron density and mean excitation energy, which are required parameters for computing proton stopping powers via the Bethe–Bloch equation. The authors compared the stopping power estimation accuracymore » of the BVM with that of a nonlinear, nonseparable photon cross section Torikoshi parametric fit model (VCU tPFM) as implemented by the authors and by Yang et al. [“Theoretical variance analysis of single- and dual-energy computed tomography methods for calculating proton stopping power ratios of biological tissues,” Phys. Med. Biol. 55, 1343–1362 (2010)]. Using an idealized monoenergetic DECT imaging model, proton ranges estimated by the BVM, VCU tPFM, and Yang tPFM were compared to International Commission on Radiation Units and Measurements (ICRU) published reference values. The robustness of the stopping power prediction accuracy of tissue composition variations was assessed for both of the BVM and VCU tPFM. The sensitivity of accuracy to CT image uncertainty was also evaluated. Results: Based on the authors’ idealized, error-free DECT imaging model, the root-mean-square error of BVM proton stopping power estimation for 175 MeV protons relative to ICRU reference values for 34 ICRU standard tissues is 0.20%, compared to 0.23% and 0.68% for the Yang and VCU tPFM models, respectively. The range estimation errors were less than 1 mm for the BVM and Yang tPFM models, respectively. The BVM estimation accuracy is not dependent on tissue type and proton energy range. The BVM is slightly more vulnerable to CT image intensity uncertainties than the tPFM models. Both the BVM and tPFM prediction accuracies were robust to uncertainties of tissue composition and independent of the choice of reference values. This reported accuracy does not include the impacts of I-value uncertainties and imaging artifacts and may not be achievable on current clinical CT scanners. Conclusions: The proton stopping power estimation accuracy of the proposed linear, separable BVM model is comparable to or better than that of the nonseparable tPFM models proposed by other groups. In contrast to the tPFM, the BVM does not require an iterative solving for effective atomic number and electron density at every voxel; this improves the computational efficiency of DECT imaging when iterative, model-based image reconstruction algorithms are used to minimize noise and systematic imaging artifacts of CT images.« less
  • Purpose: To extend the two-parameter separable basis-vector model (BVM) to estimation of proton stopping power from dual-energy CT (DECT) imaging. Methods: BVM assumes that the photon cross sections of any unknown material can be represented as a linear combination of the corresponding quantities for two bracketing basis materials. We show that both the electron density (ρe) and mean excitation energy (Iex) can be modeled by BVM, enabling stopping power to be estimated from the Bethe-Bloch equation. We have implemented an idealized post-processing dual energy imaging (pDECT) simulation consisting of monogenetic 45 keV and 80 keV scanning beams with polystyrene-water andmore » water-CaCl2 solution basis pairs for soft tissues and bony tissues, respectively. The coefficients of 24 standard ICRU tissue compositions were estimated by pDECT. The corresponding ρe, Iex, and stopping power tables were evaluated via BVM and compared to tabulated ICRU 44 reference values. Results: BVM-based pDECT was found to estimate ρe and Iex with average and maximum errors of 0.5% and 2%, respectively, for the 24 tissues. Proton stopping power values at 175 MeV, show average/maximum errors of 0.8%/1.4%. For adipose, muscle and bone, these errors result range prediction accuracies less than 1%. Conclusion: A new two-parameter separable DECT model (BVM) for estimating proton stopping power was developed. Compared to competing parametric fit DECT models, BVM has the comparable prediction accuracy without necessitating iterative solution of nonlinear equations or a sample-dependent empirical relationship between effective atomic number and Iex. Based on the proton BVM, an efficient iterative statistical DECT reconstruction model is under development.« less
  • Purpose: The conversion of Hounsfield Unit (HU) to proton stopping power ratio (SPR) is a main source of uncertainty in proton therapy. In this study, the SPRs of animal tissues were measured and compared with prediction from dual energy CT (DECT) and single energy CT (SECT) calibrations. Methods: A stoichiometric calibration method for DECT was applied to predict the SPR using CT images acquired at 80 kVp and 140 kVp. The dual energy index was derived based on the HUs of the paired spectral images and used to calculate the SPRs of the materials. Tissue surrogates with known chemical compositionsmore » were used for calibration, and animal tissues (pig brain, liver, kidney; veal shank, muscle) were used for validation. The materials were irradiated with proton pencil beams, and SPRs were deduced from the residual proton range measured using a multi-layer ion chamber device. In addition, Gafchromic EBT3 films were used to measure the distal dose profiles after irradiation through the tissue samples and compared with those calculated by the treatment planning system using both DECT and SECT predicted SPRs. Results: The differences in SPR between DECT prediction and measurement were −0.31±0.36% for bone, 0.47±0.42% for brain, 0.67±0.15% for liver, 0.51±0.52% for kidney, and −0.96±0.15% for muscle. The corresponding results using SECT were 3.1±0.12%, 1.90±0.45%, −0.66±0.11%, 2.33±0.21%, and −1.70±0.17%. In the film measurements, average distances between film and calculated distal dose profiles were 0.35±0.12 mm for DECT calibration and −1.22±0.12 mm for SECT calibration for a beam with a range of 15.79 cm. Conclusion: Our study indicates that DECT is superior to SECT for proton SPR prediction and has the potential to reduce the range uncertainty to less than 2%. DECT may permit the use of tighter distal and proximal range uncertainty margins for treatment, thereby increasing the precision of proton therapy.« less
  • The triplet and singlet low-energy parameters in the effective-range expansion for neutron-proton scattering are determined by using the latest experimental data on respective phase shifts from the SAID nucleon-nucleon database. The results differ markedly from the analogous parameters obtained on the basis of the phase shifts of the Nijmegen group and contradict the parameter values that are presently used as experimental ones. The values found with the aid of the phase shifts from the SAID nucleon-nucleon database for the total cross section for the scattering of zero-energy neutrons by protons, {sigma}{sub 0} = 20.426 b, and the neutron-proton coherent scatteringmore » length, f = -3.755 fm, agree perfectly with experimental cross-section values obtained by Houk, {sigma}{sub 0} = 20.436 {+-} 0.023 b, and experimental scattering-length values obtained by Houk and Wilson, f = -3.756 {+-} 0.009 fm, but they contradict cross-section values of {sigma}{sub 0} = 20.491 {+-} 0.014 b according to Dilg and coherent-scattering-length values of f = -3.7409 {+-} 0.0011 fm according to Koester and Nistler.« less
  • A modified local-plasma model, based on the works of Lindhard and Winther, and Bethe, Brown, and Walske is established. The Gordon-Kim model for molecular-electron density is used to calculate stopping power of N/sub 2/, O/sub 2/, and water vapor for protons of energy ranging from 40 keV to 2.5 MeV, resulting in good agreement with experimental data. Deviations from Bragg's rule are evaluated and are discussed under the present theoretical model.