skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

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. Wed . "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 = {Wed Jun 15 00:00:00 EDT 2016},
month = {Wed Jun 15 00:00:00 EDT 2016}
}
  • 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
  • Purpose: To evaluate the clinical performance of dual-energy CT (DECT) in determining proton stopping power ratios (SPR) and demonstrate advantages over conventional single-energy CT (SECT). Methods: SECT and DECT scans of tissue-equivalent plastics as well as animal meat samples are performed with a Siemens SOMATOM Definition Flash. The methods of Schneider et al. (1996) and Bourque et al. (2014) are used to determine proton SPR on SECT and DECT images, respectively. Waterequivalent path length (WEPL) measurements of plastics and tissue samples are performed with a 195 MeV proton beam. WEPL values are determined experimentally using the depth-dose shift and dosemore » extinction methods. Results: Comparison between CT-based and experimental WEPL is performed for 12 tissue-equivalent plastic as well as 6 meat boxes containing animal liver, kidney, heart, stomach, muscle and bones. For plastic materials, results show a systematic improvement in determining SPR with DECT, with a mean absolute error of 0.4% compared to 1.7% for SECT. For the meat samples, preliminary results show the ability for DECT to determine WEPL with a mean absolute value of 1.1% over all meat boxes. Conclusion: This work demonstrates the potential in using DECT for determining proton SPR with plastic materials in a clinical context. Further work is required to show the benefits of DECT for tissue samples. While experimental uncertainties could be a limiting factor to show the benefits of DECT over SECT for the meat samples, further work is required to adapt the DECT formalism in the context of clinical use, where noise and artifacts play an important role.« less
  • Purpose: The relative stopping power (RSP) uncertainty is the largest contributor to the range uncertainty in proton therapy. The purpose of this work is to develop a robust and systematic method that yields accurate patient specific RSPs by combining pre-treatment X-ray CT and daily proton radiography. Methods: The method is formulated as a penalized least squares optimization (PLSO) problem min(|Ax-B|). The matrix A represents the cumulative path-length crossed in each material computed by calculating proton trajectories through the X-ray CT. The material RSPs are denoted by x and B is the pRad, expressed as water equivalent thickness. The equation ismore » solved using a convex-conic optimizer. Geant4 simulations were made to assess the feasibility of the method. RSP extracted from the Geant4 materials were used as a reference and the clinical HU-RSP curve as a comparison. The PLSO was first tested on a Gammex RMI-467 phantom. Then, anthropomorphic phantoms of the head, pelvis and lung were studied and resulting RSPs were evaluated. A pencil beam was generated in each phantom to evaluate the proton range accuracy achievable by using the optimized RSPs. Finally, experimental data of a pediatric head phantom (CIRS) were acquired using a recently completed experimental pCT scanner. Results: Numerical simulations showed precise RSP (<0.75%) for Gammex materials except low-density lung (LN-300) (1.2%). Accurate RSP have been obtained for the head (µ=−0.10%, 1.5σ=1.12%), lung (µ=−0.33, 1.5σ=1.02%) and pelvis anthropomorphic phantoms (µ=0.12, 1.5σ=0,99%). The range precision has been improved with an average R80 difference to the reference (µ±1.5σ) of −0.20±0.35%, −0.47±0.92% and −0.06±0.17% in the head, lung and pelvis phantoms respectively, compared to the 3.5% clinical margin. Experimental HU-RSP curve have been produced on the CIRS pediatric head. Conclusion: The proposed PLSO with prior knowledge X-ray CT shows promising potential (R80 σ<1.0% over all sites) to decrease the range uncertainty.« less
  • Purpose: The aim of this study was to investigate whether the stopping power ratio (SPR) of a deformable, silicone-based 3D dosimeter could be determined more accurately using dual energy (DE) CT compared to using conventional methods based on single energy (SE) CT. The use of SECT combined with the stoichiometric calibration method was therefore compared to DECT-based determination. Methods: The SPR of the dosimeter was estimated based on its Hounsfield units (HUs) in both a SECT image and a DECT image set. The stoichiometric calibration method was used for converting the HU in the SECT image to a SPR valuemore » for the dosimeter while two published SPR calibration methods for dual energy were applied on the DECT images. Finally, the SPR of the dosimeter was measured in a 60 MeV proton by quantifying the range difference with and without the dosimeter in the beam path. Results: The SPR determined from SECT and the stoichiometric method was 1.10, compared to 1.01 with both DECT calibration methods. The measured SPR for the dosimeter material was 0.97. Conclusions: The SPR of the dosimeter was overestimated by 13% using the stoichiometric method and by 3% when using DECT. If the stoichiometric method should be applied for the dosimeter, the HU of the dosimeter must be manually changed in the treatment planning system in order to give a correct SPR estimate. Using a wrong SPR value will cause differences between the calculated and the delivered treatment plans.« less
  • 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