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Title: TU-AB-BRC-03: Accurate Tissue Characterization for Monte Carlo Dose Calculation Using Dual-and Multi-Energy CT Data

Abstract

Purpose: To develop a general method for human tissue characterization with dual-and multi-energy CT and evaluate its performance in determining elemental compositions and the associated proton stopping power relative to water (SPR) and photon mass absorption coefficients (EAC). Methods: Principal component analysis is used to extract an optimal basis of virtual materials from a reference dataset of tissues. These principal components (PC) are used to perform two-material decomposition using simulated DECT data. The elemental mass fraction and the electron density in each tissue is retrieved by measuring the fraction of each PC. A stoichiometric calibration method is adapted to the technique to make it suitable for clinical use. The present approach is compared with two others: parametrization and three-material decomposition using the water-lipid-protein (WLP) triplet. Results: Monte Carlo simulations using TOPAS for four reference tissues shows that characterizing them with only two PC is enough to get a submillimetric precision on proton range prediction. Based on the simulated DECT data of 43 references tissues, the proposed method is in agreement with theoretical values of protons SPR and low-kV EAC with a RMS error of 0.11% and 0.35%, respectively. In comparison, parametrization and WLP respectively yield RMS errors of 0.13% andmore » 0.29% on SPR, and 2.72% and 2.19% on EAC. Furthermore, the proposed approach shows potential applications for spectral CT. Using five PC and five energy bins reduces the SPR RMS error to 0.03%. Conclusion: The proposed method shows good performance in determining elemental compositions from DECT data and physical quantities relevant to radiotherapy dose calculation and generally shows better accuracy and unbiased results compared to reference methods. The proposed method is particularly suitable for Monte Carlo calculations and shows promise in using more than two energies to characterize human tissue with CT.« less

Authors:
;  [1]
  1. University of Montreal, Montreal, Qc (Canada)
Publication Date:
OSTI Identifier:
22653932
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; ANIMAL TISSUES; COMPUTERIZED SIMULATION; ERRORS; MONTE CARLO METHOD; PERFORMANCE; PLANT TISSUES; PRODUCTIVITY; PROTONS; RADIATION DOSES

Citation Formats

Lalonde, A, and Bouchard, H. TU-AB-BRC-03: Accurate Tissue Characterization for Monte Carlo Dose Calculation Using Dual-and Multi-Energy CT Data. United States: N. p., 2016. Web. doi:10.1118/1.4957397.
Lalonde, A, & Bouchard, H. TU-AB-BRC-03: Accurate Tissue Characterization for Monte Carlo Dose Calculation Using Dual-and Multi-Energy CT Data. United States. doi:10.1118/1.4957397.
Lalonde, A, and Bouchard, H. 2016. "TU-AB-BRC-03: Accurate Tissue Characterization for Monte Carlo Dose Calculation Using Dual-and Multi-Energy CT Data". United States. doi:10.1118/1.4957397.
@article{osti_22653932,
title = {TU-AB-BRC-03: Accurate Tissue Characterization for Monte Carlo Dose Calculation Using Dual-and Multi-Energy CT Data},
author = {Lalonde, A and Bouchard, H},
abstractNote = {Purpose: To develop a general method for human tissue characterization with dual-and multi-energy CT and evaluate its performance in determining elemental compositions and the associated proton stopping power relative to water (SPR) and photon mass absorption coefficients (EAC). Methods: Principal component analysis is used to extract an optimal basis of virtual materials from a reference dataset of tissues. These principal components (PC) are used to perform two-material decomposition using simulated DECT data. The elemental mass fraction and the electron density in each tissue is retrieved by measuring the fraction of each PC. A stoichiometric calibration method is adapted to the technique to make it suitable for clinical use. The present approach is compared with two others: parametrization and three-material decomposition using the water-lipid-protein (WLP) triplet. Results: Monte Carlo simulations using TOPAS for four reference tissues shows that characterizing them with only two PC is enough to get a submillimetric precision on proton range prediction. Based on the simulated DECT data of 43 references tissues, the proposed method is in agreement with theoretical values of protons SPR and low-kV EAC with a RMS error of 0.11% and 0.35%, respectively. In comparison, parametrization and WLP respectively yield RMS errors of 0.13% and 0.29% on SPR, and 2.72% and 2.19% on EAC. Furthermore, the proposed approach shows potential applications for spectral CT. Using five PC and five energy bins reduces the SPR RMS error to 0.03%. Conclusion: The proposed method shows good performance in determining elemental compositions from DECT data and physical quantities relevant to radiotherapy dose calculation and generally shows better accuracy and unbiased results compared to reference methods. The proposed method is particularly suitable for Monte Carlo calculations and shows promise in using more than two energies to characterize human tissue with CT.},
doi = {10.1118/1.4957397},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = 2016,
month = 6
}
  • Purpose: An improvement in tissue assignment for low-dose rate brachytherapy (LDRB) patients using more accurate Monte Carlo (MC) dose calculation was accomplished with a metallic artifact reduction (MAR) method specific to dual-energy computed tomography (DECT). Methods: The proposed MAR algorithm followed a four-step procedure. The first step involved applying a weighted blend of both DECT scans (I {sub H/L}) to generate a new image (I {sub Mix}). This action minimized Hounsfield unit (HU) variations surrounding the brachytherapy seeds. In the second step, the mean HU of the prostate in I {sub Mix} was calculated and shifted toward the mean HUmore » of the two original DECT images (I {sub H/L}). The third step involved smoothing the newly shifted I {sub Mix} and the two original I {sub H/L}, followed by a subtraction of both, generating an image that represented the metallic artifact (I {sub A,(H/L)}) of reduced noise levels. The final step consisted of subtracting the original I {sub H/L} from the newly generated I {sub A,(H/L)} and obtaining a final image corrected for metallic artifacts. Following the completion of the algorithm, a DECT stoichiometric method was used to extract the relative electronic density (ρ{sub e}) and effective atomic number (Z {sub eff}) at each voxel of the corrected scans. Tissue assignment could then be determined with these two newly acquired physical parameters. Each voxel was assigned the tissue bearing the closest resemblance in terms of ρ{sub e} and Z {sub eff}, comparing with values from the ICRU 42 database. A MC study was then performed to compare the dosimetric impacts of alternative MAR algorithms. Results: An improvement in tissue assignment was observed with the DECT MAR algorithm, compared to the single-energy computed tomography (SECT) approach. In a phantom study, tissue misassignment was found to reach 0.05% of voxels using the DECT approach, compared with 0.40% using the SECT method. Comparison of the DECT and SECT D {sub 90} dose parameter (volume receiving 90% of the dose) indicated that D {sub 90} could be underestimated by up to 2.3% using the SECT method. Conclusions: The DECT MAR approach is a simple alternative to reduce metallic artifacts found in LDRB patient scans. Images can be processed quickly and do not require the determination of x-ray spectra. Substantial information on density and atomic number can also be obtained. Furthermore, calcifications within the prostate are detected by the tissue assignment algorithm. This enables more accurate, patient-specific MC dose calculations.« less
  • Purpose: To develop a new segmentation technique using dual energy CT (DECT) to overcome limitations related to segmentation from a standard Hounsfield unit (HU) to electron density (ED) calibration curve. Both methods are compared with a Monte Carlo analysis of dose distribution. Methods: DECT allows a direct calculation of both ED and effective atomic number (EAN) within a given voxel. The EAN is here defined as a function of the total electron cross-section of a medium. These values can be effectively acquired using a calibrated method from scans at two different energies. A prior stoichiometric calibration on a Gammex RMImore » phantom allows us to find the parameters to calculate EAN and ED within a voxel. Scans from a Siemens SOMATOM Definition Flash dual source system provided the data for our study. A Monte Carlo analysis compares dose distribution simulated by dosxyz-nrc, considering a head phantom defined by both segmentation techniques. Results: Results from depth dose and dose profile calculations show that materials with different atomic compositions but similar EAN present differences of less than 1%. Therefore, it is possible to define a short list of basis materials from which density can be adapted to imitate interaction behavior of any tissue. Comparison of the dose distributions on both segmentations shows a difference of 50% in dose in areas surrounding bone at low energy. Conclusion: The presented segmentation technique allows a more accurate medium definition in each voxel, especially in areas of tissue transition. Since the behavior of human tissues is highly sensitive at low energies, this reduces the errors on calculated dose distribution. This method could be further developed to optimize the tissue characterization based on anatomic site.« less
  • Purpose: the objective of this study was to assess utilizing water dose point kernel (DPK)instead of tissue dose point kernels in convolution algorithms.to the best of our knowledge, in providing 3D distribution of absorbed dose from a 3D distribution of the activity, the human body is considered equivalent to water. as a Result tissue variations are not considered in patient specific dosimetry. Methods: In this study Gate v7.0 was used to calculate tissue dose point kernel. the beta emitter radionuclides which have taken into consideration in this simulation include Y-90, Lu-177 and P-32 which are commonly used in nuclear medicine.more » the comparison has been performed for dose point kernels of adipose, bone, breast, heart, intestine, kidney, liver, lung and spleen versus water dose point kernel. Results: In order to validate the simulation the Result of 90Y DPK in water were compared with published results of Papadimitroulas et al (Med. Phys., 2012). The results represented that the mean differences between water DPK and other soft tissues DPKs range between 0.6 % and 1.96% for 90Y, except for lung and bone, where the observed discrepancies are 6.3% and 12.19% respectively. The range of DPK difference for 32P is between 1.74% for breast and 18.85% for bone. For 177Lu, the highest difference belongs to bone which is equal to 16.91%. For other soft tissues the least discrepancy is observed in kidney with 1.68%. Conclusion: In all tissues except for lung and bone, the results of GATE for dose point kernel were comparable to water dose point kernel which demonstrates the appropriateness of applying water dose point kernel instead of soft tissues in the field of nuclear medicine.« less
  • Purpose: To improve Monte Carlo dose calculation accuracy through a new tissue segmentation technique with dual energy CT (DECT). Methods: Electron density (ED) and effective atomic number (EAN) can be extracted directly from DECT data with a stoichiometric calibration method. Images are acquired with Monte Carlo CT projections using the user code egs-cbct and reconstructed using an FDK backprojection algorithm. Calibration is performed using projections of a numerical RMI phantom. A weighted parameter algorithm then uses both EAN and ED to assign materials to voxels from DECT simulated images. This new method is compared to a standard tissue characterization frommore » single energy CT (SECT) data using a segmented calibrated Hounsfield unit (HU) to ED curve. Both methods are compared to the reference numerical head phantom. Monte Carlo simulations on uniform phantoms of different tissues using dosxyz-nrc show discrepancies in depth-dose distributions. Results: Both SECT and DECT segmentation methods show similar performance assigning soft tissues. Performance is however improved with DECT in regions with higher density, such as bones, where it assigns materials correctly 8% more often than segmentation with SECT, considering the same set of tissues and simulated clinical CT images, i.e. including noise and reconstruction artifacts. Furthermore, Monte Carlo results indicate that kV photon beam depth-dose distributions can double between two tissues of density higher than muscle. Conclusions: A direct acquisition of ED and the added information of EAN with DECT data improves tissue segmentation and increases the accuracy of Monte Carlo dose calculation in kV photon beams.« less