DOSE DISTRIBUTION IN A BREAST UNDERGOING MAMMOGRAPHY BASED ON A 3D DETAILED BREAST MODEL FOR CHINESE WOMEN

DOSE DISTRIBUTION IN A BREAST UNDERGOING MAMMOGRAPHY BASED ON A 3D DETAILED BREAST MODEL FOR... Abstract Not only the mean glandular dose (MGD) but also the glandular dose distribution is important in describing the radiation exposure to breast in mammography. For a more precise knowledge of the absorbed dose distribution in the breast, experimental measurements with thermoluminescence dosemeter and Monte Carlo simulations with Geant4 were performed in this study. The experimental measurements with homogeneous physical breast phantoms were used to validate Monte Carlo simulations of homogeneous mathematical breast models undergoing mammography. Then a 3D detailed breast model with a compressed breast thickness of 4 cm and a glandular content of 50%, which has been constructed in previous work, was used to study the absorbed dose distribution inside the breast undergoing mammography. Furthermore, the effects of the glandular tissue distribution on MGD were studied by reversing the breast model in head–toe direction to get a breast model with a different distribution of glandular tissues. INTRODUCTION Mammography is regarded as one of the most effective diagnostic methods for the early detection of breast cancer.(1) However, there is a risk of radiation-induced breast cancer in mammography. The average dose to the glandular tissue (mean glandular dose (MGD)) is used to estimate the risk of breast cancer induced by ionizing radiation by ACR,(2) EUREF(3) and IAEA.(4) It is estimated by the following equation:   MGD=K×DgN, (1)where K is the measured incident air kerma at the upper surface of a breast, and DgN is the K to MGD conversion coefficient calculated using Monte Carlo codes. The DgN values for breasts of different glandular contents and different compressed breast thicknesses (CBTs) were presented by Dance et al.,(5–8) which were adopted by the IAEA and EUREF protocols. The DgN values presented by Wu et al.,(9, 10) which were adopted by the ACR protocol, were also list by glandular content and CBT. The Monte Carlo simulations were all based on a simple model of breast developed by Hammerstein et al.(11) The simple model had a central region composed of a uniform mixture of adipose and glandular tissues and a surrounding layer of adipose tissue that simulates the skin. However, the average dose to the glandular tissue, MGD could not represent the inhomogeneity of the glandular dose distribution within the breast. A low energy spectrum is used in mammography, which causes that the absorbed dose in breast decreases rapidly with the increasing depth from the upper surface of the breast. Therefore, the depth dose distribution in breast, which is expressed as percent depth dose (PDD) or relative dose, has been widely concerned. PDD is the depth dose normalized to the dose at the upper surface of a breast. Relative dose is the dose normalized to the MGD. The depth dose distribution in the breast undergoing mammography has been studied with thermoluminescence dosemeter (TLD) measurements and Monte Carlo simulations.(12–16) Homogeneous breast phantoms were used in those studies and the distribution of glandular tissues within the breast was not considered. The MGD and the glandular dose distribution depend not only on the glandular content and the CBT, but also on the distribution of glandular tissues within the breast.(17) A 3D detailed breast model with realistic structures in the breast has been constructed based on the Chinese female breast parameters in previous work.(18) It was used to estimate MGD for mammography of Chinese women. In this study, an experiment with TLD measurements in homogeneous physical breast phantoms was performed to validate Monte Carlo simulations of mammography with Geant4. Then the 3D detailed breast model constructed in previous work was used to study the dose distribution inside the breast undergoing mammography. Furthermore, it was used to study the effects of glandular tissue distribution within the breast on MGD in mammography. MATERIALS AND METHODS Validation of Monte Carlo simulation Dose measurements with TLD The GE Senographe DS MAMMOMAT was used to perform the measurements. A Mo target with a 0.03 mm Mo filter was chosen. Half value layers (HVLs) at different X-ray tube voltages (25, 28, 30 and 32 kV) were obtained from the measurements with TLD GR200A (7LiF: Mg, Cu, P), which was 4.5 mm in diameter and 0.8 mm thick. Taking into account the contribution of the efficiency correction factor, the reader calibration factor and the stability of the quality control correction factor, typical uncertainties for these TLDs measurements are of ~10%.(16) PDD values in physics breast phantoms of 3, 4.5 and 6 cm thicknesses in mammography were obtained from the measurements. The CIRS breast phantoms were used to perform the measurements, which included soft tissue equivalent slabs enabling a range of thickness from 0.5 to 7 cm. PDD values at 28 kV were obtained from the measurements with placing TLDs at different depth values in the breast phantoms. Figure 1 shows the equipment used in the measurements. Figure 2 shows the sketch of depth dose measurements with a 4.5 cm breast phantom. Figure 1. View largeDownload slide Equipment used for measurements: the GE Senographe DS MAMMOMAT (left), the CIRS breast phantom (upper right), and the TLD GR200A (lower right). Figure 1. View largeDownload slide Equipment used for measurements: the GE Senographe DS MAMMOMAT (left), the CIRS breast phantom (upper right), and the TLD GR200A (lower right). Figure 2. View largeDownload slide Sketch of depth dose measurements with a 4.5 cm CIRS breast phantom. Figure 2. View largeDownload slide Sketch of depth dose measurements with a 4.5 cm CIRS breast phantom. Monte Carlo simulations with Geant4 In this study, the Monte Carlo code Geant4 Version 9.6 was used to present the simulations. Figure 3 shows the sketch of the geometry in the simulations of mammography. Photon started from a point source at a distance of 65 cm from the image receptor plane. It was generated isotropically over the support paddle, a field size of 180 mm × 240 mm. The cut-off limit for the lower electron and photon in Geant4 was set to 0.1 mm in consideration of calculation accuracy and efficiency. The compressed paddle was simulated as a 2.4 mm thick polycarbonate slab and the breast support was simulated as a 4.1 mm thick carbon fiber slab referring to Dance et al.(7) The X-ray spectra data were taken from Boone et al.(19). Figure 3. View largeDownload slide Sketch of the geometry in the simulations of mammography. Figure 3. View largeDownload slide Sketch of the geometry in the simulations of mammography. HVLs at different X-ray tube voltages (25, 28, 30 and 32 kV) were obtained from the Monte Carlo simulations. The 1 × 108 photons were simulated, which led to the statistical error of Monte Carlo simulations being below 2% (1.14–1.79%). PDD values at 28 kV in breast phantoms of different thicknesses (3, 4.5 and 6 cm) were obtained from the Monte Carlo simulations. The 3 × 108 photons were simulated, which led to the statistical error of Monte Carlo simulations being below 5% (0.56–4.28%). Simulation of dose distribution in a 3D detailed breast model A 3D detailed breast model As mentioned in previous work, it is reasonable to represent an ‘average breast’ of Chinese women with a breast model with a CBT of 4 cm and a glandularity of 50%.(18) Therefore, a 3D detailed breast model with a CBT of 4 cm and a glandularity of 50%, which has been constructed for a more precise breast dose estimation in mammography in previous work, was used to study the dose distribution in the breast undergoing mammography. It was a voxel model with a voxel size of 0.2 mm × 0.2 mm × 0.081 mm. The structures in the detailed breast model included skin, subcutaneous fat, Cooper’s ligaments, intraglandular fats, ductal trees and lobules, retromammary fat and so on. The 3D detailed breast model is shown in Figure 4. Only the breast shape and the ductal system are shown. The densities for breast tissue materials (Table 1) were taken from the ICRU 46 report.(20) Figure 4. View largeDownload slide A 3D detailed breast model with only the breast shape and the ductal system shown. Figure 4. View largeDownload slide A 3D detailed breast model with only the breast shape and the ductal system shown. Table 1. Densities for breast tissue materials in the 3D detailed breast model. Breast tissue  Density (g/cm3)  Skin  1.09  Adipose tissue  0.95  Glandular tissue  1.02  Cooper’s ligament  1.05  Breast tissue  Density (g/cm3)  Skin  1.09  Adipose tissue  0.95  Glandular tissue  1.02  Cooper’s ligament  1.05  Table 1. Densities for breast tissue materials in the 3D detailed breast model. Breast tissue  Density (g/cm3)  Skin  1.09  Adipose tissue  0.95  Glandular tissue  1.02  Cooper’s ligament  1.05  Breast tissue  Density (g/cm3)  Skin  1.09  Adipose tissue  0.95  Glandular tissue  1.02  Cooper’s ligament  1.05  Simulation of depth dose distribution With the detailed breast model, relative dose, which is the dose deposited in the glandular tissue voxels at different depth values nomalized to the MGD, were calculated at different HVLs. The 3 × 108 photons were simulated, which led to the statistical error of Monte Carlo simulations at most of the depth values being below 1%. Simulation of 3D dose distribution The 3D distribution of the relative dose was calculated in this study. In consideration of calculation accuracy and efficiency, the dose deposited in each 1000 voxels (10 × 10 × 10 voxels) of the breast model was scored together. The 2.4 × 1010 photons were simulated, which led to the statistical error of most of the voxels being below 2%. Simulation to study the effect of glandular tissue distribution on MGD Reverse of the detailed breast model MGD depends not only on the glandular content and the CBT but also on the distribution of glandular tissues within the breast. To study the effects of the distribution of glandular tissues on MGD, the detailed compressed breast model was reversed in head–toe direction, so that it had the same CBT and glandularity but a different distribution of glandular tissues. The distributions of glandular tissues in the breast model and the reverse model are shown in Figure 5. The glandular tissue distribution in a homogeneous breast model (Breast-homo) is also shown. It shows that glandular tissues in the reverse model (Breast-rev) are concentrated in the upper part of the model. The difference between the distributions of glandular tissues in the central part of the models (1–3 cm) is not obvious. Figure 5. View largeDownload slide Glandular tissue volume percent at different depth value of the detailed breast model (Breast), the reverse model (Breast-rev) and the homogeneous model (Breast-homo). Figure 5. View largeDownload slide Glandular tissue volume percent at different depth value of the detailed breast model (Breast), the reverse model (Breast-rev) and the homogeneous model (Breast-homo). Calculation of glandular tissue dose conversion coefficient To study the effects of the glandular tissue distribution on MGD, the incident air kerma at the upper surface of the breast to MGD conversion coefficients were calculated with the detailed compressed breast model and the reverse model at different HVLs (0.341 mm Al, 0.380 mm Al, 0.402 mm Al and 0.416 mm Al). The 3 × 106 photons were simulated, which led to the statistical error of Monte Carlo simulations being below 1% (0.41–0.81%). RESULTS AND DISCUSSION Validation of Monte Carlo simulation The simulations of mammography with Geant4 were validated by the TLD measurements. The comparison between HVLs measured and simulated at different X-ray tube voltages is shown in Figure 6. The values obtained from the simulations are in good agreement with those obtained from the measurements. The maximum difference is 2.3%. Figure 6. View largeDownload slide Comparison between HVLs measured with TLD and those simulated with Geant4. Figure 6. View largeDownload slide Comparison between HVLs measured with TLD and those simulated with Geant4. Measurements and simulations were performed to obtain PDD values at 28 kV in homogeneous physical breast phantoms of 3, 4.5 and 6 cm thicknesses undergoing mammography. The comparison of the PDD values is shown in Figure 7. The maximum difference between the measured value and the simulated value for different breast phantom is shown in Table 2. The difference is <3.8% at a depth of 0.5 cm. The difference tends to be larger at a larger depth from the upper surface of the breast because both the measurement error and the simulation error are larger in deeper depths. The measurement error is larger due to the rapid change of beam quality with depth in the phantom. The simulation error is larger in deeper depths where less particles were scored. However, the difference is no more than 14.5% at the lower surface of the breast, where the measured PDD value is only 0.0133 for 6 cm breast phantom. Figure 7. View largeDownload slide Comparison between PDD values at 28 kV measured with TLD and these simulated with Geant4. Figure 7. View largeDownload slide Comparison between PDD values at 28 kV measured with TLD and these simulated with Geant4. Table 2. Maximum difference between the measured value and the simulated value. CBT of breast phantom (cm)  Depth (cm)  Maximum difference (%)  3  0, 0.5, 2.5, 3.0  7.6  4.5  0, 0.5, 2.0, 4.0, 4.5  10.5  6  0, 0.5, 2.0, 4.0, 5.5, 6.0  14.5  CBT of breast phantom (cm)  Depth (cm)  Maximum difference (%)  3  0, 0.5, 2.5, 3.0  7.6  4.5  0, 0.5, 2.0, 4.0, 4.5  10.5  6  0, 0.5, 2.0, 4.0, 5.5, 6.0  14.5  Table 2. Maximum difference between the measured value and the simulated value. CBT of breast phantom (cm)  Depth (cm)  Maximum difference (%)  3  0, 0.5, 2.5, 3.0  7.6  4.5  0, 0.5, 2.0, 4.0, 4.5  10.5  6  0, 0.5, 2.0, 4.0, 5.5, 6.0  14.5  CBT of breast phantom (cm)  Depth (cm)  Maximum difference (%)  3  0, 0.5, 2.5, 3.0  7.6  4.5  0, 0.5, 2.0, 4.0, 4.5  10.5  6  0, 0.5, 2.0, 4.0, 5.5, 6.0  14.5  Dose distribution in a 3D detailed breast model Simulation of depth dose distribution Depth dose distribution, which was expressed as relative dose, was calculated with the detailed breast model. Figure 8a shows the results calculated with a homogeneous breast model and the detailed breast model at 0.380 mm Al. The simulation error is shown in the upper right corner of the figure. The difference between the results is shown in Figure 8b, which is 10.4–19.9%. As an average of the dose deposited in the glandular tissue voxels at the same depth value from the upper surface of the breast, the depth dose distribution in the detailed breast model shows the same tendency with that in a homogeneous breast model. The difference is mainly attributed to the different MGDs calculated with different models. Previous study shows that the MGD calculated with the detailed breast model is lower(18), therefore, the relative dose calculated with the detailed breast model is larger than that calculated with the homogeneous breast model. Figure 8. View largeDownload slide Comparison between relative dose simulated with a detailed breast model (RDdetailed) and those simulated with a homogeneous breast model (RDhomo) at 0.380 mm Al. Figure 8. View largeDownload slide Comparison between relative dose simulated with a detailed breast model (RDdetailed) and those simulated with a homogeneous breast model (RDhomo) at 0.380 mm Al. Figure 9 shows the results calculated with the detailed breast model at different HVLs with Geant4. The simulation error is shown in the upper right corner of the figure. Depth dose changes less rapidly for a larger HVL or a larger X-ray tube voltage. Figure 9. View largeDownload slide Relative dose simulated with a detailed breast model at different HVLs. Figure 9. View largeDownload slide Relative dose simulated with a detailed breast model at different HVLs. Calculation of 3D dose distribution With the detailed compressed breast model, 3D distribution of the relative dose in the breast undergoing mammography was calculated, which is shown in Figure 10a. The simulation error with depth in the phantom is shown in Figure 10b. The HVL was 0.402 mm Al. The dose in each voxel decreases rapidly with the increasing depth from the upper surface of the breast. The dose to the upper surface of the breast is more than six times of the MGD of the breast while the dose to the lower surface of the breast is <10% of the MGD. The dose to the voxels with a larger density tends to be larger, such as Cooper’s ligaments, which were modeled as elongated ellipsoid shells in the detailed breast model.(18) Figure 10. View largeDownload slide A 3D distribution of the relative dose in the detailed breast model at 0.402 mm Al in mammography. Figure 10. View largeDownload slide A 3D distribution of the relative dose in the detailed breast model at 0.402 mm Al in mammography. The 2D distribution of the relative dose in different slice of different direction is shown in Figure 11. The density for Cooper’s ligament or glandular tissue is larger, therefore, the dose to the voxel made up of Cooper’s ligament or glandular tissue is larger, such as the ductal trees and lobules in Figure 11b and e and the Cooper’s ligaments in Figure 11a and c. Figure 11. View largeDownload slide 2D distributions of the relative dose in different slice of different direction in the detailed breast model in mammography, (a) the slice at 4 mm depth from the upper surface of the breast in the head–toe direction, (b) the slice in the middle plane in the head–toe direction, (c) the slice adjacent to chest wall in the chest–nipple direction, (d) the slice in the middle plane in the chest–nipple direction and (e) the slice in the middle plane in the lateral direction. Figure 11. View largeDownload slide 2D distributions of the relative dose in different slice of different direction in the detailed breast model in mammography, (a) the slice at 4 mm depth from the upper surface of the breast in the head–toe direction, (b) the slice in the middle plane in the head–toe direction, (c) the slice adjacent to chest wall in the chest–nipple direction, (d) the slice in the middle plane in the chest–nipple direction and (e) the slice in the middle plane in the lateral direction. Percentage of the glandular tissue voxels with relative dose below a certain value in all glandular tissue voxels of the breast model is shown in Figure 12. It shows that the dose of 10% glandular tissue voxels is smaller than 0.4 times of MGD, and the dose of 10% glandular tissue voxels is larger than 1.6 times of MGD. The middle value is ~0.8. Figure 12. View largeDownload slide Percentage of the glandular tissue voxels with relative dose below a certain value. Figure 12. View largeDownload slide Percentage of the glandular tissue voxels with relative dose below a certain value. Effect of glandular tissue distribution on MGD To study the effects of the distribution of glandular tissues on MGD, glandular tissue dose conversion coefficients calculated with the detailed compressed breast model (cc) and the reverse model (cc-rev) were calculated with Geant4. The results at different HVLs are shown in Figure 13. The conversion coefficients obtained from Dance et al.(6) are also shown. Figure 13. View largeDownload slide Glandular tissue dose conversion coefficients calculated with the detailed compressed breast model (cc), calculated with the reverse model (cc-rev), and obtained from Dance et al. Figure 13. View largeDownload slide Glandular tissue dose conversion coefficients calculated with the detailed compressed breast model (cc), calculated with the reverse model (cc-rev), and obtained from Dance et al. It shows that glandular tissue dose conversion coefficients are ~10% larger when the breast model is reversed. It is because that glandular tissues in the reverse model are concentrated in the upper part of the model from Figure 5. Glandular tissue dose conversion coefficients calculated with the reverse model are more consistent with these obtained from Dance et al.(6) and it consists better when the HVL is larger. The adipose fat layer in the reverse model is ~5 mm (Figure 5), which is more consistent with the simple breast model used by Dance et al.(6), therefore, the values calculated with the reverse model are more consistent with these obtained from Dance et al..(6) The glandular tissues in the reverse model are concentrated in depth from 1.5 to 2.5 cm. The glandular tissues in the simple breast model distribute evenly in depth from 0.5 to 3.5 cm. Relative depth doses change less rapidly for a larger HVL as shown in Figure 9, therefore, it consists better for a larger HVL. CONCLUSION The simulations with Geant4 were validated through an experiment with TLDs in this study. For a more precise knowledge of the absorbed dose distribution in the breast, a detailed compressed breast model with a CBT of 4 cm and a glandularity of 50%, which has been constructed in previous work was used to study the dose distribution in breast in mammography. The effects of the distribution of glandular tissues in breast on MGD were also studied with the detailed breast model. A more precise knowledge of the absorbed dose distribution in the breast undergoing mammography was presented in this study, which provides more detailed information about risk assessment for Chinese women undergoing mammography. FUNDING This work was supported by the National Natural Science Foundation of China [Grant nos. 11375103 and 11275110]. and the Subject of National Science and Technology Major Project of China [Grant no. 2013ZX06002001-007]. The authors appreciate support for this article by the Collaborative Innovation Center of Public Safety. REFERENCES 1 Lauby-Secretan, B., Scoccianti, C., Loomis, D., Benbrahim-Tallaa, L., Bouvard, V., Bianchini, F. and Straif, K. International Agency for Research on Cancer Handbook Working Group. Breast-cancer screening-viewpoint of the IARC Working Group. N. Engl. J. Med.  372, 2353– 2358 ( 2015). Google Scholar CrossRef Search ADS PubMed  2 ACR. Mammography Quality Control Manual  ( America: American College of Radiology) ( 1999). 3 EUREF. European Guidelines for Quality Assurance in Breast Cancer Screening and Diagnosis  ( The Netherlands: European Communities)) ( 2003). 4 IAEA. Dosimetry in diagnostic radiology: an international code of practice. Technical reports series no. 457 ( 2006). 5 Dance, D. R. Monte Carlo calculation of conversion factors for the estimation of mean glandular breast dose. Phys. Med. Biol.  35, 1211– 1219 ( 1990). Google Scholar CrossRef Search ADS PubMed  6 Dance, D. R., Skinner, C. L., Young, K. C., Beckett, J. R. and Kotre, C. J. Additional factors for the estimation of mean glandular breast dose using the UK mammography dosimetry protocol. Phys. Med. Biol.  45, 3225– 3240 ( 2000). Google Scholar CrossRef Search ADS PubMed  7 Dance, D. R., Skinner, C. L., Young, K. C. and Van Engen, R. E. Further factors for the estimation of mean glandular dose using the United Kingdom, European and IAEA breast dosimetry protocols. Phys. Med. Biol.  54, 4361– 4372 ( 2009). Google Scholar CrossRef Search ADS PubMed  8 Dance, D. R. and Young, K. C. Estimation of mean glandular dose for contrast enhanced digital mammography: factors for use with the UK, European and IAEA breast dosimetry protocols. Phys. Med. Biol.  59, 2127– 2137 ( 2014). Google Scholar CrossRef Search ADS PubMed  9 Wu, X., Barnes, G. T. and Tucker, D. M. Spectral dependence of glandular tissue dose in screen-film mammography. Radiology  179, 143– 148 ( 1991). Google Scholar CrossRef Search ADS PubMed  10 Wu, X., Gingold, E. L., Barnes, G. T. and Tucker, D. M. Normalized average glandular dose in molybdenum target-rhodium filter and rhodium target-rhodium filter mammography. Radiology  193, 83– 89 ( 1994). Google Scholar CrossRef Search ADS PubMed  11 Hammerstein, G. R., Miller, D. W., White, D. R., Masterson, M. E., Woodard, H. Q. and Laughlin, J. S. Absorbed radiation dose in mammography. Radiology  130, 485– 491 ( 1979). Google Scholar CrossRef Search ADS PubMed  12 Delis, H., Spyrou, G., Tzanakos, G. and Panayiotakis, G. The influence of mammographic X-ray spectra on absorbed energy distribution in breast: Monte Carlo simulation studies. Radiat. Meas.  39, 149– 155 ( 2005). Google Scholar CrossRef Search ADS   13 Tsai, H. Y., Chong, N. S., Ho, Y. J. and Tyan, Y. S. Evaluation of depth dose and glandular dose for digital mammography. Radiat. Meas.  45, 726– 728 ( 2010). Google Scholar CrossRef Search ADS   14 Camargo-Mendoza, R. E., Poletti, M. E., Costa, A. M. and Caldas, L. V. E. Measurement of some dosimetric parameters for two mammography systems using thermoluminescent dosimetry. Radiat. Meas.  46, 2086– 2089 ( 2011). Google Scholar CrossRef Search ADS   15 Nigapruke, K., Puwanich, P., Phaisangittisakul, N. and Youngdee, W. Monte Carlo simulation of average glandular dose and an investigation of influencing factors. Radiat. Res.  51, 441– 448 ( 2010). Google Scholar CrossRef Search ADS   16 Di Maria, S., Barros, S., Bento, J., Teles, P., Figueira, C., Pereira, M., Vaz, P. and Paulo, G. TLD measurements and Monte Carlo simulations for glandular dose and scatter fraction assessment in mammography: a comparative study. Radiat. Meas.  46, 1103– 1108 ( 2011). Google Scholar CrossRef Search ADS   17 Zankl, M., Fill, U., Hoeschen, C., Panzer, W. and Regulla, D. Average glandular dose conversion coefficients for segmented breast voxel models. Radiat. Prot. Dosim.  114, 406– 409 ( 2005). Google Scholar CrossRef Search ADS   18 Wang, W., Qiu, R., Ren, L., Wu, Z., Li, C., Niu, Y. and Li, J. Monte Carlo calculation of conversion coefficients for dose estimation in mammography based on a 3D detailed breast model. Med. Phys.  44, 2503– 2514 ( 2017). Google Scholar CrossRef Search ADS PubMed  19 Boone, J. M., Fewell, T. R. and Jennings, R. J. Molybdenum, rhodium and tungsten anode spectral models using interpolating polynomials with application to mammography. Med. Phys.  24, 1863– 1874 ( 1997). Google Scholar CrossRef Search ADS PubMed  20 ICRU. Photon, electron, proton and neutron interaction data for body tissues. ICRU Report 46, 1992. © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Radiation Protection Dosimetry Oxford University Press

DOSE DISTRIBUTION IN A BREAST UNDERGOING MAMMOGRAPHY BASED ON A 3D DETAILED BREAST MODEL FOR CHINESE WOMEN

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Abstract

Abstract Not only the mean glandular dose (MGD) but also the glandular dose distribution is important in describing the radiation exposure to breast in mammography. For a more precise knowledge of the absorbed dose distribution in the breast, experimental measurements with thermoluminescence dosemeter and Monte Carlo simulations with Geant4 were performed in this study. The experimental measurements with homogeneous physical breast phantoms were used to validate Monte Carlo simulations of homogeneous mathematical breast models undergoing mammography. Then a 3D detailed breast model with a compressed breast thickness of 4 cm and a glandular content of 50%, which has been constructed in previous work, was used to study the absorbed dose distribution inside the breast undergoing mammography. Furthermore, the effects of the glandular tissue distribution on MGD were studied by reversing the breast model in head–toe direction to get a breast model with a different distribution of glandular tissues. INTRODUCTION Mammography is regarded as one of the most effective diagnostic methods for the early detection of breast cancer.(1) However, there is a risk of radiation-induced breast cancer in mammography. The average dose to the glandular tissue (mean glandular dose (MGD)) is used to estimate the risk of breast cancer induced by ionizing radiation by ACR,(2) EUREF(3) and IAEA.(4) It is estimated by the following equation:   MGD=K×DgN, (1)where K is the measured incident air kerma at the upper surface of a breast, and DgN is the K to MGD conversion coefficient calculated using Monte Carlo codes. The DgN values for breasts of different glandular contents and different compressed breast thicknesses (CBTs) were presented by Dance et al.,(5–8) which were adopted by the IAEA and EUREF protocols. The DgN values presented by Wu et al.,(9, 10) which were adopted by the ACR protocol, were also list by glandular content and CBT. The Monte Carlo simulations were all based on a simple model of breast developed by Hammerstein et al.(11) The simple model had a central region composed of a uniform mixture of adipose and glandular tissues and a surrounding layer of adipose tissue that simulates the skin. However, the average dose to the glandular tissue, MGD could not represent the inhomogeneity of the glandular dose distribution within the breast. A low energy spectrum is used in mammography, which causes that the absorbed dose in breast decreases rapidly with the increasing depth from the upper surface of the breast. Therefore, the depth dose distribution in breast, which is expressed as percent depth dose (PDD) or relative dose, has been widely concerned. PDD is the depth dose normalized to the dose at the upper surface of a breast. Relative dose is the dose normalized to the MGD. The depth dose distribution in the breast undergoing mammography has been studied with thermoluminescence dosemeter (TLD) measurements and Monte Carlo simulations.(12–16) Homogeneous breast phantoms were used in those studies and the distribution of glandular tissues within the breast was not considered. The MGD and the glandular dose distribution depend not only on the glandular content and the CBT, but also on the distribution of glandular tissues within the breast.(17) A 3D detailed breast model with realistic structures in the breast has been constructed based on the Chinese female breast parameters in previous work.(18) It was used to estimate MGD for mammography of Chinese women. In this study, an experiment with TLD measurements in homogeneous physical breast phantoms was performed to validate Monte Carlo simulations of mammography with Geant4. Then the 3D detailed breast model constructed in previous work was used to study the dose distribution inside the breast undergoing mammography. Furthermore, it was used to study the effects of glandular tissue distribution within the breast on MGD in mammography. MATERIALS AND METHODS Validation of Monte Carlo simulation Dose measurements with TLD The GE Senographe DS MAMMOMAT was used to perform the measurements. A Mo target with a 0.03 mm Mo filter was chosen. Half value layers (HVLs) at different X-ray tube voltages (25, 28, 30 and 32 kV) were obtained from the measurements with TLD GR200A (7LiF: Mg, Cu, P), which was 4.5 mm in diameter and 0.8 mm thick. Taking into account the contribution of the efficiency correction factor, the reader calibration factor and the stability of the quality control correction factor, typical uncertainties for these TLDs measurements are of ~10%.(16) PDD values in physics breast phantoms of 3, 4.5 and 6 cm thicknesses in mammography were obtained from the measurements. The CIRS breast phantoms were used to perform the measurements, which included soft tissue equivalent slabs enabling a range of thickness from 0.5 to 7 cm. PDD values at 28 kV were obtained from the measurements with placing TLDs at different depth values in the breast phantoms. Figure 1 shows the equipment used in the measurements. Figure 2 shows the sketch of depth dose measurements with a 4.5 cm breast phantom. Figure 1. View largeDownload slide Equipment used for measurements: the GE Senographe DS MAMMOMAT (left), the CIRS breast phantom (upper right), and the TLD GR200A (lower right). Figure 1. View largeDownload slide Equipment used for measurements: the GE Senographe DS MAMMOMAT (left), the CIRS breast phantom (upper right), and the TLD GR200A (lower right). Figure 2. View largeDownload slide Sketch of depth dose measurements with a 4.5 cm CIRS breast phantom. Figure 2. View largeDownload slide Sketch of depth dose measurements with a 4.5 cm CIRS breast phantom. Monte Carlo simulations with Geant4 In this study, the Monte Carlo code Geant4 Version 9.6 was used to present the simulations. Figure 3 shows the sketch of the geometry in the simulations of mammography. Photon started from a point source at a distance of 65 cm from the image receptor plane. It was generated isotropically over the support paddle, a field size of 180 mm × 240 mm. The cut-off limit for the lower electron and photon in Geant4 was set to 0.1 mm in consideration of calculation accuracy and efficiency. The compressed paddle was simulated as a 2.4 mm thick polycarbonate slab and the breast support was simulated as a 4.1 mm thick carbon fiber slab referring to Dance et al.(7) The X-ray spectra data were taken from Boone et al.(19). Figure 3. View largeDownload slide Sketch of the geometry in the simulations of mammography. Figure 3. View largeDownload slide Sketch of the geometry in the simulations of mammography. HVLs at different X-ray tube voltages (25, 28, 30 and 32 kV) were obtained from the Monte Carlo simulations. The 1 × 108 photons were simulated, which led to the statistical error of Monte Carlo simulations being below 2% (1.14–1.79%). PDD values at 28 kV in breast phantoms of different thicknesses (3, 4.5 and 6 cm) were obtained from the Monte Carlo simulations. The 3 × 108 photons were simulated, which led to the statistical error of Monte Carlo simulations being below 5% (0.56–4.28%). Simulation of dose distribution in a 3D detailed breast model A 3D detailed breast model As mentioned in previous work, it is reasonable to represent an ‘average breast’ of Chinese women with a breast model with a CBT of 4 cm and a glandularity of 50%.(18) Therefore, a 3D detailed breast model with a CBT of 4 cm and a glandularity of 50%, which has been constructed for a more precise breast dose estimation in mammography in previous work, was used to study the dose distribution in the breast undergoing mammography. It was a voxel model with a voxel size of 0.2 mm × 0.2 mm × 0.081 mm. The structures in the detailed breast model included skin, subcutaneous fat, Cooper’s ligaments, intraglandular fats, ductal trees and lobules, retromammary fat and so on. The 3D detailed breast model is shown in Figure 4. Only the breast shape and the ductal system are shown. The densities for breast tissue materials (Table 1) were taken from the ICRU 46 report.(20) Figure 4. View largeDownload slide A 3D detailed breast model with only the breast shape and the ductal system shown. Figure 4. View largeDownload slide A 3D detailed breast model with only the breast shape and the ductal system shown. Table 1. Densities for breast tissue materials in the 3D detailed breast model. Breast tissue  Density (g/cm3)  Skin  1.09  Adipose tissue  0.95  Glandular tissue  1.02  Cooper’s ligament  1.05  Breast tissue  Density (g/cm3)  Skin  1.09  Adipose tissue  0.95  Glandular tissue  1.02  Cooper’s ligament  1.05  Table 1. Densities for breast tissue materials in the 3D detailed breast model. Breast tissue  Density (g/cm3)  Skin  1.09  Adipose tissue  0.95  Glandular tissue  1.02  Cooper’s ligament  1.05  Breast tissue  Density (g/cm3)  Skin  1.09  Adipose tissue  0.95  Glandular tissue  1.02  Cooper’s ligament  1.05  Simulation of depth dose distribution With the detailed breast model, relative dose, which is the dose deposited in the glandular tissue voxels at different depth values nomalized to the MGD, were calculated at different HVLs. The 3 × 108 photons were simulated, which led to the statistical error of Monte Carlo simulations at most of the depth values being below 1%. Simulation of 3D dose distribution The 3D distribution of the relative dose was calculated in this study. In consideration of calculation accuracy and efficiency, the dose deposited in each 1000 voxels (10 × 10 × 10 voxels) of the breast model was scored together. The 2.4 × 1010 photons were simulated, which led to the statistical error of most of the voxels being below 2%. Simulation to study the effect of glandular tissue distribution on MGD Reverse of the detailed breast model MGD depends not only on the glandular content and the CBT but also on the distribution of glandular tissues within the breast. To study the effects of the distribution of glandular tissues on MGD, the detailed compressed breast model was reversed in head–toe direction, so that it had the same CBT and glandularity but a different distribution of glandular tissues. The distributions of glandular tissues in the breast model and the reverse model are shown in Figure 5. The glandular tissue distribution in a homogeneous breast model (Breast-homo) is also shown. It shows that glandular tissues in the reverse model (Breast-rev) are concentrated in the upper part of the model. The difference between the distributions of glandular tissues in the central part of the models (1–3 cm) is not obvious. Figure 5. View largeDownload slide Glandular tissue volume percent at different depth value of the detailed breast model (Breast), the reverse model (Breast-rev) and the homogeneous model (Breast-homo). Figure 5. View largeDownload slide Glandular tissue volume percent at different depth value of the detailed breast model (Breast), the reverse model (Breast-rev) and the homogeneous model (Breast-homo). Calculation of glandular tissue dose conversion coefficient To study the effects of the glandular tissue distribution on MGD, the incident air kerma at the upper surface of the breast to MGD conversion coefficients were calculated with the detailed compressed breast model and the reverse model at different HVLs (0.341 mm Al, 0.380 mm Al, 0.402 mm Al and 0.416 mm Al). The 3 × 106 photons were simulated, which led to the statistical error of Monte Carlo simulations being below 1% (0.41–0.81%). RESULTS AND DISCUSSION Validation of Monte Carlo simulation The simulations of mammography with Geant4 were validated by the TLD measurements. The comparison between HVLs measured and simulated at different X-ray tube voltages is shown in Figure 6. The values obtained from the simulations are in good agreement with those obtained from the measurements. The maximum difference is 2.3%. Figure 6. View largeDownload slide Comparison between HVLs measured with TLD and those simulated with Geant4. Figure 6. View largeDownload slide Comparison between HVLs measured with TLD and those simulated with Geant4. Measurements and simulations were performed to obtain PDD values at 28 kV in homogeneous physical breast phantoms of 3, 4.5 and 6 cm thicknesses undergoing mammography. The comparison of the PDD values is shown in Figure 7. The maximum difference between the measured value and the simulated value for different breast phantom is shown in Table 2. The difference is <3.8% at a depth of 0.5 cm. The difference tends to be larger at a larger depth from the upper surface of the breast because both the measurement error and the simulation error are larger in deeper depths. The measurement error is larger due to the rapid change of beam quality with depth in the phantom. The simulation error is larger in deeper depths where less particles were scored. However, the difference is no more than 14.5% at the lower surface of the breast, where the measured PDD value is only 0.0133 for 6 cm breast phantom. Figure 7. View largeDownload slide Comparison between PDD values at 28 kV measured with TLD and these simulated with Geant4. Figure 7. View largeDownload slide Comparison between PDD values at 28 kV measured with TLD and these simulated with Geant4. Table 2. Maximum difference between the measured value and the simulated value. CBT of breast phantom (cm)  Depth (cm)  Maximum difference (%)  3  0, 0.5, 2.5, 3.0  7.6  4.5  0, 0.5, 2.0, 4.0, 4.5  10.5  6  0, 0.5, 2.0, 4.0, 5.5, 6.0  14.5  CBT of breast phantom (cm)  Depth (cm)  Maximum difference (%)  3  0, 0.5, 2.5, 3.0  7.6  4.5  0, 0.5, 2.0, 4.0, 4.5  10.5  6  0, 0.5, 2.0, 4.0, 5.5, 6.0  14.5  Table 2. Maximum difference between the measured value and the simulated value. CBT of breast phantom (cm)  Depth (cm)  Maximum difference (%)  3  0, 0.5, 2.5, 3.0  7.6  4.5  0, 0.5, 2.0, 4.0, 4.5  10.5  6  0, 0.5, 2.0, 4.0, 5.5, 6.0  14.5  CBT of breast phantom (cm)  Depth (cm)  Maximum difference (%)  3  0, 0.5, 2.5, 3.0  7.6  4.5  0, 0.5, 2.0, 4.0, 4.5  10.5  6  0, 0.5, 2.0, 4.0, 5.5, 6.0  14.5  Dose distribution in a 3D detailed breast model Simulation of depth dose distribution Depth dose distribution, which was expressed as relative dose, was calculated with the detailed breast model. Figure 8a shows the results calculated with a homogeneous breast model and the detailed breast model at 0.380 mm Al. The simulation error is shown in the upper right corner of the figure. The difference between the results is shown in Figure 8b, which is 10.4–19.9%. As an average of the dose deposited in the glandular tissue voxels at the same depth value from the upper surface of the breast, the depth dose distribution in the detailed breast model shows the same tendency with that in a homogeneous breast model. The difference is mainly attributed to the different MGDs calculated with different models. Previous study shows that the MGD calculated with the detailed breast model is lower(18), therefore, the relative dose calculated with the detailed breast model is larger than that calculated with the homogeneous breast model. Figure 8. View largeDownload slide Comparison between relative dose simulated with a detailed breast model (RDdetailed) and those simulated with a homogeneous breast model (RDhomo) at 0.380 mm Al. Figure 8. View largeDownload slide Comparison between relative dose simulated with a detailed breast model (RDdetailed) and those simulated with a homogeneous breast model (RDhomo) at 0.380 mm Al. Figure 9 shows the results calculated with the detailed breast model at different HVLs with Geant4. The simulation error is shown in the upper right corner of the figure. Depth dose changes less rapidly for a larger HVL or a larger X-ray tube voltage. Figure 9. View largeDownload slide Relative dose simulated with a detailed breast model at different HVLs. Figure 9. View largeDownload slide Relative dose simulated with a detailed breast model at different HVLs. Calculation of 3D dose distribution With the detailed compressed breast model, 3D distribution of the relative dose in the breast undergoing mammography was calculated, which is shown in Figure 10a. The simulation error with depth in the phantom is shown in Figure 10b. The HVL was 0.402 mm Al. The dose in each voxel decreases rapidly with the increasing depth from the upper surface of the breast. The dose to the upper surface of the breast is more than six times of the MGD of the breast while the dose to the lower surface of the breast is <10% of the MGD. The dose to the voxels with a larger density tends to be larger, such as Cooper’s ligaments, which were modeled as elongated ellipsoid shells in the detailed breast model.(18) Figure 10. View largeDownload slide A 3D distribution of the relative dose in the detailed breast model at 0.402 mm Al in mammography. Figure 10. View largeDownload slide A 3D distribution of the relative dose in the detailed breast model at 0.402 mm Al in mammography. The 2D distribution of the relative dose in different slice of different direction is shown in Figure 11. The density for Cooper’s ligament or glandular tissue is larger, therefore, the dose to the voxel made up of Cooper’s ligament or glandular tissue is larger, such as the ductal trees and lobules in Figure 11b and e and the Cooper’s ligaments in Figure 11a and c. Figure 11. View largeDownload slide 2D distributions of the relative dose in different slice of different direction in the detailed breast model in mammography, (a) the slice at 4 mm depth from the upper surface of the breast in the head–toe direction, (b) the slice in the middle plane in the head–toe direction, (c) the slice adjacent to chest wall in the chest–nipple direction, (d) the slice in the middle plane in the chest–nipple direction and (e) the slice in the middle plane in the lateral direction. Figure 11. View largeDownload slide 2D distributions of the relative dose in different slice of different direction in the detailed breast model in mammography, (a) the slice at 4 mm depth from the upper surface of the breast in the head–toe direction, (b) the slice in the middle plane in the head–toe direction, (c) the slice adjacent to chest wall in the chest–nipple direction, (d) the slice in the middle plane in the chest–nipple direction and (e) the slice in the middle plane in the lateral direction. Percentage of the glandular tissue voxels with relative dose below a certain value in all glandular tissue voxels of the breast model is shown in Figure 12. It shows that the dose of 10% glandular tissue voxels is smaller than 0.4 times of MGD, and the dose of 10% glandular tissue voxels is larger than 1.6 times of MGD. The middle value is ~0.8. Figure 12. View largeDownload slide Percentage of the glandular tissue voxels with relative dose below a certain value. Figure 12. View largeDownload slide Percentage of the glandular tissue voxels with relative dose below a certain value. Effect of glandular tissue distribution on MGD To study the effects of the distribution of glandular tissues on MGD, glandular tissue dose conversion coefficients calculated with the detailed compressed breast model (cc) and the reverse model (cc-rev) were calculated with Geant4. The results at different HVLs are shown in Figure 13. The conversion coefficients obtained from Dance et al.(6) are also shown. Figure 13. View largeDownload slide Glandular tissue dose conversion coefficients calculated with the detailed compressed breast model (cc), calculated with the reverse model (cc-rev), and obtained from Dance et al. Figure 13. View largeDownload slide Glandular tissue dose conversion coefficients calculated with the detailed compressed breast model (cc), calculated with the reverse model (cc-rev), and obtained from Dance et al. It shows that glandular tissue dose conversion coefficients are ~10% larger when the breast model is reversed. It is because that glandular tissues in the reverse model are concentrated in the upper part of the model from Figure 5. Glandular tissue dose conversion coefficients calculated with the reverse model are more consistent with these obtained from Dance et al.(6) and it consists better when the HVL is larger. The adipose fat layer in the reverse model is ~5 mm (Figure 5), which is more consistent with the simple breast model used by Dance et al.(6), therefore, the values calculated with the reverse model are more consistent with these obtained from Dance et al..(6) The glandular tissues in the reverse model are concentrated in depth from 1.5 to 2.5 cm. The glandular tissues in the simple breast model distribute evenly in depth from 0.5 to 3.5 cm. Relative depth doses change less rapidly for a larger HVL as shown in Figure 9, therefore, it consists better for a larger HVL. CONCLUSION The simulations with Geant4 were validated through an experiment with TLDs in this study. For a more precise knowledge of the absorbed dose distribution in the breast, a detailed compressed breast model with a CBT of 4 cm and a glandularity of 50%, which has been constructed in previous work was used to study the dose distribution in breast in mammography. The effects of the distribution of glandular tissues in breast on MGD were also studied with the detailed breast model. A more precise knowledge of the absorbed dose distribution in the breast undergoing mammography was presented in this study, which provides more detailed information about risk assessment for Chinese women undergoing mammography. FUNDING This work was supported by the National Natural Science Foundation of China [Grant nos. 11375103 and 11275110]. and the Subject of National Science and Technology Major Project of China [Grant no. 2013ZX06002001-007]. The authors appreciate support for this article by the Collaborative Innovation Center of Public Safety. REFERENCES 1 Lauby-Secretan, B., Scoccianti, C., Loomis, D., Benbrahim-Tallaa, L., Bouvard, V., Bianchini, F. and Straif, K. International Agency for Research on Cancer Handbook Working Group. Breast-cancer screening-viewpoint of the IARC Working Group. N. Engl. J. Med.  372, 2353– 2358 ( 2015). Google Scholar CrossRef Search ADS PubMed  2 ACR. Mammography Quality Control Manual  ( America: American College of Radiology) ( 1999). 3 EUREF. European Guidelines for Quality Assurance in Breast Cancer Screening and Diagnosis  ( The Netherlands: European Communities)) ( 2003). 4 IAEA. Dosimetry in diagnostic radiology: an international code of practice. Technical reports series no. 457 ( 2006). 5 Dance, D. R. Monte Carlo calculation of conversion factors for the estimation of mean glandular breast dose. Phys. Med. Biol.  35, 1211– 1219 ( 1990). 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Radiation Protection DosimetryOxford University Press

Published: Feb 9, 2018

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