臺灣博碩士論文加值系統

三維乳房造影技術改善了二維影像的組織重疊問題，然而不可忽視進行乳房電腦斷層 (Breast computed tomography, bCT) 檢查可能造成的致癌風險，因此乳腺劑量評估是重要的議題。本研究建立一系列的擬真軟體乳房假體 (Anthropomorphic software breast phantoms, ASBPs) 且利用蒙地卡羅技術模擬bCT以評估對應的乳腺劑量轉換因子 (Normalized glandular dose, DgN)。ASBPs有500、750與1000 ml三種乳房大小及10%、20%、30%、40%與50%五種乳腺體積比例 (Volumetric glandular fraction, VGF)，且利用蒙地卡羅技術建構bCT的幾何模型，並模擬ASBP內乳腺組織的能量沉積，以計算10-70 keV下間隔5 keV的單能DgN，與50、60與70 kVp下X光能譜的多能DgN。為了探討乳房內部組織分布對於DgN的影響，比較均質與異質ASBPs的DgN差異。除此之外，評估蒙地卡羅模擬時，投影角度與取樣間隔對於DgN的影響。結果顯示，單能DgN與光子能量呈正相關，與VGF亦呈正關係，500與750 ml乳房體積的平均DgN比值為1.235，而1000與750 ml則為0.838。在50、60與70 kVp下的平均多能DgN則分別為0.560、0.627與0.670。針對均質與異質ASBPs之DgN比較，在50、60與70 kVp下，多能DgN的平均比值分別為1.152、1.111與1.085。當射束角度為頭尾 (Cranio-caudal, CC) 方向時相對DgN最小，當取樣數為4時，所產生DgN會低估約3.2%。本研究建立了15種乳房體積與VGF組合的ASBPs，這些假體可以模仿脂肪層與乳腺組織的分布，以準確地評估bCT所造成的平均乳腺劑量 (Mean glandular dose, MGD) 與DgN。

Three-dimensional breast imaging techniques improve the problem of overlapping tissue in two-dimensional images. However, the risk of cancer caused by breast computed tomography (bCT) examination cannot be ignored. Therefore, the glandular dose assessment is an important issue. This study built a series of anthropomorphic software breast phantoms (ASBPs) and estimated the corresponding normalized glandular dose (DgN) for bCT with Monte Carlo simulation. There were ASBPs of three different breast sizes (500, 750, and 1000 ml) and five volumetric glandular fractions (VGFs) (10%, 20%, 30%, 40%, and 50%). A geometry for bCT was constructed using the Monte Carlo technique. Simulation of energy deposition in the glandular tissue of ASBP was conducted to obtain monoenergetic DgN from 10 to 70 keV at 5 keV intervals, and polyenergetic DgN of 50, 60, and 70-kVp X-ray spectra. To investigate the effect of breast tissue distribution on DgN, comparative analyses between homogeneous and heterogeneous ASBPs were performed for DgN values. Moreover, the effects of projection angle and sampling interval on DgN were assessed with Monte Carlo simulation. A positive correlation was observed between the monoenergetic DgN and photon energy. The monoenergetic DgN and VGF also correlated positively. The average DgN ratio of 500 to 750-ml breast volume was 1.235, and that of 1000 and 750-ml breast volume was 0.838. The polyenergetic DgN values at 50, 60, and 70 kVp were 0.560, 0.627, and 0.670 on average, respectively. In the comparison of DgN between homogeneous and heterogeneous ASBPs, the average ratios of polyenergetic DgN were 1.152, 1.111, and 1.085 at 50, 60, and 70 kVp, respectively. When the beam angle was in the cranio-caudal (CC) direction, the lowest relative DgN value was attained. When the sampling number was four, there was DgN underestimation of 3.2% approximately. In this study, ASBPs with 15 combinations of breast volume and VGF were constructed. These phantoms mimic distribution of the adipose and glandular tissues in the breast and thus enable accurate evaluation of the mean glandular dose (MGD) and DgN for bCT.

致 謝.....i
摘 要.....ii
Abstract.....iii
目 錄.....v
圖目錄.....vii
表目錄.....x
1.前言.....1
1.1.研究目的.....1
1.2.研究架構.....7
2.文獻回顧.....9
2.1.三維乳房造影技術的演進與發展.....9
2.1.1.乳房斷層攝影機 (Digital breast tomosynthesis, DBT).....9
2.1.2.乳房電腦斷層 (Breast computed tomography, bCT).....10
2.2.乳房電腦斷層之乳腺劑量評估.....12
2.3.均質與異質乳房假體的乳腺劑量評估之差異.....20
3.材料與方法.....24
3.1.擬真軟體乳房假體 (Anthropomorphic software breast phantom, ASBP).....24
3.2.蒙地卡羅模擬.....26
3.2.1.平均乳腺劑量 (Mean glandular dose, MGD).....30
3.2.2.空氣克馬 (Air kerma).....33
3.3.乳腺劑量轉換因子 (Normalized glandular dose, DgN).....33
3.4.蒙地卡羅驗證.....35
3.5.均質與異質擬真軟體乳房假體之劑量比較.....36
3.6.投影角度、取樣間隔與乳腺劑量轉換因子的關係.....37
4.結果.....38
4.1.擬真軟體乳房假體.....38
4.2.蒙地卡羅驗證.....40
4.3.擬真軟體乳房假體之乳腺劑量轉換因子.....46
4.4.均質與異質擬真軟體乳房假體之劑量比較.....52
4.5.投影角度、取樣間隔與乳腺劑量轉換因子的關係.....55
5.討論.....58
5.1.擬真軟體乳房假體的優缺點.....58
5.2.蒙地卡羅驗證.....59
5.3.擬真軟體乳房假體之乳腺劑量轉換因子.....60
5.4.均質與異質擬真軟體乳房假體之劑量比較.....60
5.5.投影角度、取樣間隔與乳腺劑量轉換因子的關係.....61
5.6.限制.....61
6.結論.....63
7.致謝.....65
8.參考文獻.....66

圖目錄
圖1-1、國際癌症研究機構 (International Agency for Research on Cancer, IARC) 2018年公布之GLOBOCAN (a)全球癌症發病率(b)全球癌症死亡率(c)女性癌症發病率(d)女性癌症死亡率。.....3
圖1-2、衛生福利部國民健康署統計民國101-106年台灣國人乳房X光攝影篩檢數。.....4
圖1-3、乳房解剖構造圖，其中乳腺組織集中於乳房中心，外層有脂肪層環繞，最外層則是皮膚層。.....5
圖1-4、實驗流程架構圖。.....7
圖2-1、乳房斷層攝影機DBT，其機頭會垂直偵檢器進行小角度的轉動。.....10
圖2-2、乳房電腦斷層bCT，機器內部有X光管與偵檢器，以360度旋轉掃描。.....11
圖2-3、Boone等人實驗室內的bCT幾何結構，SIC為38.4 cm。.....14
圖2-4、柯寧公司Koning Corp.商用的bCT幾何結構，SIC為65 cm。.....16
圖2-5、Yi等人實驗室內簡易的bCT幾何，SIC為75 cm。.....17
圖2-6、V1-V6乳房均質假體的外型幾何圖。.....19
圖2-7、Hernandez等人利用bCT影像及雙高斯方程式繪製出的乳房內部組織分布圖。.....21
圖3-1、ASBP的(a)矢狀切面與(b)橫切面，其中包含腺體生長區、脂肪生長區與皮膚層。.....24
圖3-2、bCT系統的幾何結構。(a) xy軸面與(b) xz軸面，其中SIC為50.2 cm，射源前方5 cm處放置10 cm厚的鉛板。.....27
圖3-3、在管電壓為50 kVp下的能譜圖。.....34
圖3-4、(a)乳房X光攝影的幾何結構、(b)乳房數學假體的橫切面與(c)乳房數學假體的冠狀面。.....35
圖3-5、以重複結構填充的乳房體素假體。.....36
圖4-1、ASBPs的矢狀面。由上到下，分別為500 ml、750 ml與1000 ml，由左至右，乳腺體積比例分別為10%、20%、30%、40%與50%。.....38
圖4-2、750 ml且VGF 20%的ASBP立體圖，外層綠色區為皮膚層，內部淺藍色區為腺體生長區，紅色線為韌帶，其他區域則為脂肪生長區。.....39
圖4-3、三層乳房數學假體與由(a) 4.4×1.9×0.4、(b) 0.4×0.4×0.4與(c) 0.4×0.2×0.4 cm3三種體素大小建構之乳房體素假體的乳房劑量關係圖。.....41
圖4-4、Hernandez等人所計算的實驗結果與本次實驗結果的關係圖。本次實驗結果分別採用(a) AAPM TG 195報告與(b) Johns所著作的書籍內提供的數據資料，以計算在10.25、20.25、30.25、40.25、50.25與60.25 keV單能光子下的DgN。.....43
圖4-5、均質ASBP與Hernandez等人真實乳房外型假體的DgN比較圖，兩者的線性擬合程度佳。.....45
圖4-6、VGF為(a) 10%、(b) 20%、(c) 30%、(d) 40%與(e) 50%之下，單能DgN與光子能量的關係，兩者具有顯著的正相關。除此之外，單能DgN與乳房體積呈反比的關係。不同體積的DgN比值顯示於右側y軸。.....48
圖4-7、在乳房體積為750 ml且VGF分別為20%、30%、40%與50%下，(a)異質ASBP的DgN和VGF呈正相關；(b)均質ASBP的DgN和VGF呈反相關。.....49
圖4-8、在固定腺體生長區下，乳房體積皆為500 ml，VGF則分別為40%與50%的ASBPs所模擬出的DgN，其中DgN和VGF成反相關。.....50
圖4-9、乳房體積為(a) 500 ml、(b) 750 ml與(c) 1000 ml之下，多能DgN與管電壓成顯著的正相關。除此之外，多能DgN與VGF亦為正相關。.....52
圖4-10、在50、60與70 kVp下均質真實乳房假體與異質ASBPs的多能DgN比值。箱狀的兩端分別為第一個四分位數(Q1)與第三個四分位數(Q3)，中間線為中位數(median)，交叉符號(×)為平均值。.....54
圖4-11、投影角度與相對乳腺劑量的關係。投影角度0度時相對DgN最小，在90和270度的投影角度下，相對DgN較大。.....55
圖4-12、單能DgN與取樣間隔的關係。隨著取樣間隔的上升，DgN會有低估的現象。.....56
圖4-13、在管電壓為50、60與70 kVp下，多能DgN與取樣間隔的關係。當取樣間隔大於90度時，多能DgN會有明顯低估的現象。.....57

表目錄
表2-1、由自動管電流輸出設定系統根據乳房大小與乳腺比例選擇之對應電流，單位為mA。如果乳房且乳腺過大直接假定其電流為200 mA，且於上方標誌星號(*)。.....16
表2-2、Hernandez等人統計215筆三維乳房影像的資訊，且以乳房體積分布狀況製成不同體積與形狀的V1-V6乳房均質假體。.....18
表2-3、過去不同研究中使用的簡單幾何均質假體。.....22
表3-1、皮膚、均質乳房組織、乳腺與脂肪組織元素組成與密度。.....28
表3-2、變異係數範圍定義。.....29
表3-3、紀錄符號定義。.....30
表3-4、式(10)中各項對應的係數。.....32
表4-1、Hernandez等人與本次實驗採用Johns所著作的書籍中提供的數據資料所計算之不同能量下的DgN與差異值。.....44
表4-2、在乳房體積為750 ml下，使用50、60與70 kVp三種管電壓時，均質與異質ASBPs的多能DgN倍率關係與差異。.....53

1. Bray, F., et al., Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2018. 68(6): p. 394-424.
2. 婦女乳房X光攝影檢查人數及篩檢涵蓋率. 2019, 衛生福利部國民健康署.
3. Niklason, L.T., et al., Digital tomosynthesis in breast imaging. Radiology, 1997. 205(2): p. 399-406.
4. Glick, S.J. and X. Gong. Optimal spectra for indirect detector breast tomosynthesis. in Medical Imaging 2006: Physics of Medical Imaging. 2006. International Society for Optics and Photonics.
5. Chang, C.H., et al., Computed tomography of the breast. A preliminary report. Radiology, 1977. 124(3): p. 827-9.
6. Gisvold, J.J., P.R. Karsell, and E.C. Reese, Clinical evaluation of computerized tomographic mammography. Mayo Clin Proc, 1977. 52(3): p. 181-5.
7. Chen, B. and R. Ning, Cone-beam volume CT breast imaging: feasibility study. Med Phys, 2002. 29(5): p. 755-70.
8. Boone, J., Breast CT: Its prospect for breast cancer screening and diagnosis. Advances in breast imaging: Physics, Technology and Clinical Applications, Categorical Course in Diagnostic Radiology Physics (Radiological Society of North America, Oak Brook, IL), 2004.
9. Boone, J.M., et al., Computed tomography for imaging the breast. Journal of mammary gland biology and neoplasia, 2006. 11(2): p. 103-111.
10. Ning, R., et al. A novel cone beam breast CT scanner: System evaluation. in Medical imaging 2007: physics of medical imaging. 2007. International Society for Optics and Photonics.
11. Lindfors, K.K., et al., Dedicated breast CT: initial clinical experience. Radiology, 2008. 246(3): p. 725-33.
12. O'Connell, A.M., D.L. Conover, and C.-F.L. Lin, Cone-beam computed tomography for breast imaging. Journal of Radiology Nursing, 2009. 28(1): p. 3-11.
13. ICRP, The 2007 recommendations of the International Commission on Radiological Protection. ICRP publication 103. Ann ICRP, 2007. 37(2-4): p. 1-332.
14. Boone, J.M., N. Shah, and T.R. Nelson, A comprehensive analysis of DgN(CT) coefficients for pendant-geometry cone-beam breast computed tomography. Med Phys, 2004. 31(2): p. 226-35.
15. Thacker, S.C. and S.J. Glick, Normalized glandular dose (DgN) coefficients for flat-panel CT breast imaging. Phys Med Biol, 2004. 49(24): p. 5433-44.
16. Sechopoulos, I., S.S. Feng, and C.J. D'Orsi, Dosimetric characterization of a dedicated breast computed tomography clinical prototype. Med Phys, 2010. 37(8): p. 4110-20.
17. Sechopoulos, I., et al., Characterization of the homogeneous tissue mixture approximation in breast imaging dosimetry. Med Phys, 2012. 39(8): p. 5050-9.
18. Hernandez, A.M. and J.M. Boone, Average glandular dose coefficients for pendant-geometry breast CT using realistic breast phantoms. Med Phys, 2017. 44(10): p. 5096-5105.
19. Sarno, A., et al., Monte Carlo evaluation of glandular dose in cone-beam X-ray computed tomography dedicated to the breast: Homogeneous and heterogeneous breast models. Phys Med, 2018. 51: p. 99-107.
20. Hernandez, A.M., A.E. Becker, and J.M. Boone, Updated breast CT dose coefficients (DgNCT) using patient-derived breast shapes and heterogeneous fibroglandular distributions. Med Phys, 2019. 46(3): p. 1455-1466.
21. Zhang, C., P.R. Bakic, and A.D. Maidment. Development of an anthropomorphic breast software phantom based on region growing algorithm. in Medical Imaging 2008: Visualization, Image-Guided Procedures, and Modeling. 2008. International Society for Optics and Photonics.
22. Bakic, P.R., et al., Mammogram synthesis using a 3D simulation. I. Breast tissue model and image acquisition simulation. Med Phys, 2002. 29(9): p. 2131-9.
23. Bakic, P.R., et al., Mammogram synthesis using a 3D simulation. II. Evaluation of synthetic mammogram texture. Med Phys, 2002. 29(9): p. 2140-51.
24. Bakic, P.R., et al., Mammogram synthesis using a three-dimensional simulation. III. Modeling and evaluation of the breast ductal network. Med Phys, 2003. 30(7): p. 1914-25.
25. Carton, A.K., et al., Development of a physical 3D anthropomorphic breast phantom. Med Phys, 2011. 38(2): p. 891-6.
26. Bakic, P.R., C. Zhang, and A.D. Maidment, Development and characterization of an anthropomorphic breast software phantom based upon region-growing algorithm. Med Phys, 2011. 38(6): p. 3165-76.
27. Tabar, L., et al., Swedish two-county trial: impact of mammographic screening on breast cancer mortality during 3 decades. Radiology, 2011. 260(3): p. 658-63.
28. Boyd, N.F., et al., Mammographic density and the risk and detection of breast cancer. New England Journal of Medicine, 2007. 356(3): p. 227-236.
29. McCormack, V.A. and I. dos Santos Silva, Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol Biomarkers Prev, 2006. 15(6): p. 1159-69.
30. Kolb, T.M., J. Lichy, and J.H. Newhouse, Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations. Radiology, 2002. 225(1): p. 165-75.
31. Korhonen, K.E., et al., Strategies to Increase Cancer Detection: Review of True-Positive and False-Negative Results at Digital Breast Tomosynthesis Screening. Radiographics, 2016. 36(7): p. 1954-1965.
32. Skaane, P., et al., Comparison of digital mammography alone and digital mammography plus tomosynthesis in a population-based screening program. Radiology, 2013. 267(1): p. 47-56.
33. Van Engen, R., et al., Protocol for the quality control of the physical and technical aspects of digital breast tomosynthesis system. EUREF, European Guidelines for Quality Assurance in Breast Cancer Screening and Diagnosis, 2013.
34. Seyyedi, S. and I. Yildirim, 3D digital breast tomosynthesis image reconstruction using anisotropic total variation minimization. Conf Proc IEEE Eng Med Biol Soc, 2014. 2014: p. 6052-5.
35. Rodriguez-Ruiz, A., et al., New reconstruction algorithm for digital breast tomosynthesis: better image quality for humans and computers. Acta Radiol, 2018. 59(9): p. 1051-1059.
36. Poplack, S., Breast Tomosynthesis: Clinical Evidence. Radiol Clin North Am, 2017. 55(3): p. 475-492.
37. Sechopoulos, I., et al., Computation of the glandular radiation dose in digital tomosynthesis of the breast. Med Phys, 2007. 34(1): p. 221-32.
38. Sechopoulos, I. and C.J. D'Orsi, Glandular radiation dose in tomosynthesis of the breast using tungsten targets. J Appl Clin Med Phys, 2008. 9(4): p. 2887.
39. Dance, D.R., K.C. Young, and R.E. van Engen, Estimation of mean glandular dose for breast tomosynthesis: factors for use with the UK, European and IAEA breast dosimetry protocols. Phys Med Biol, 2011. 56(2): p. 453-71.
40. Baptista, M., et al., Dosimetric characterization and organ dose assessment in digital breast tomosynthesis: Measurements and Monte Carlo simulations using voxel phantoms. Med Phys, 2015. 42(7): p. 3788-800.
41. Bouwman, R.W., et al., Average glandular dose in digital mammography and digital breast tomosynthesis: comparison of phantom and patient data. Phys Med Biol, 2015. 60(20): p. 7893-907.
42. Peppard, H.R., et al., Digital Breast Tomosynthesis in the Diagnostic Setting: Indications and Clinical Applications. Radiographics, 2015. 35(4): p. 975-90.
43. Gazi, P.M., et al., Evolution of spatial resolution in breast CT at UC Davis. Med Phys, 2015. 42(4): p. 1973-81.
44. Hammerstein, G.R., et al., Absorbed radiation dose in mammography. Radiology, 1979. 130(2): p. 485-91.
45. Dance, D.R., Monte Carlo calculation of conversion factors for the estimation of mean glandular breast dose. Phys Med Biol, 1990. 35(9): p. 1211-9.
46. Dance, D.R., et al., Additional factors for the estimation of mean glandular breast dose using the UK mammography dosimetry protocol. Phys Med Biol, 2000. 45(11): p. 3225-40.
47. Perry, N., et al., European guidelines for quality assurance in breast cancer screening and diagnosis Fourth Edition. Luxembourg: Office for Official Publications of the European Communities, 2006.
48. Wu, X., G.T. Barnes, and D.M. Tucker, Spectral dependence of glandular tissue dose in screen-film mammography. Radiology, 1991. 179(1): p. 143-8.
49. Wu, X., et al., Normalized average glandular dose in molybdenum target-rhodium filter and rhodium target-rhodium filter mammography. Radiology, 1994. 193(1): p. 83-9.
50. Boone, J.M., Glandular breast dose for monoenergetic and high-energy X-ray beams: Monte Carlo assessment. Radiology, 1999. 213(1): p. 23-37.
51. Boone, J.M., Normalized glandular dose (DgN) coefficients for arbitrary X-ray spectra in mammography: computer-fit values of Monte Carlo derived data. Med Phys, 2002. 29(5): p. 869-75.
52. Radiology, A.C.o., Mammography Quality Control Manual. 1999: American College of Radiology.
53. Yi, Y., et al., Radiation doses in cone-beam breast computed tomography: a Monte Carlo simulation study. Med Phys, 2011. 38(2): p. 589-97.
54. Dance, D.R., et al., Breast dosimetry using high-resolution voxel phantoms. Radiat Prot Dosimetry, 2005. 114(1-3): p. 359-63.
55. Hernandez, A.M., J.A. Seibert, and J.M. Boone, Breast dose in mammography is about 30% lower when realistic heterogeneous glandular distributions are considered. Med Phys, 2015. 42(11): p. 6337-48.
56. Sarno, A., et al., Homogeneous vs. patient specific breast models for Monte Carlo evaluation of mean glandular dose in mammography. Phys Med, 2018. 51: p. 56-63.
57. Huang, S.Y., et al., The effect of skin thickness determined using breast CT on mammographic dosimetry. Med Phys, 2008. 35(4): p. 1199-206.
58. Sarno, A., et al., A Monte Carlo study of monoenergetic and polyenergetic normalized glandular dose (DgN) coefficients in mammography. Phys Med Biol, 2017. 62(1): p. 306-325.
59. Kopans, D.B., Breast imaging. 2007: Lippincott Williams & Wilkins.
60. Erickson, D.W., et al., Population of 224 realistic human subject-based computational breast phantoms. Med Phys, 2016. 43(1): p. 23.
61. Yaffe, M.J., et al., The myth of the 50-50 breast. Med Phys, 2009. 36(12): p. 5437-43.
62. Koger, B. and C. Kirkby, Optimization of photon beam energies in gold nanoparticle enhanced arc radiation therapy using Monte Carlo methods. Phys Med Biol, 2016. 61(24): p. 8839-8853.
63. Reynoso, F.J., J.J. Munro Iii, and S.H. Cho, Technical Note: Monte Carlo calculations of the AAPM TG-43 brachytherapy dosimetry parameters for a new titanium-encapsulated Yb-169 source. J Appl Clin Med Phys, 2017. 18(4): p. 193-199.
64. Andreo, P., Monte Carlo simulations in radiotherapy dosimetry. Radiat Oncol, 2018. 13(1): p. 121.
65. Soares, A.D., L. Paixao, and A. Facure, Determination of the dose rate constant through Monte Carlo simulations with voxel phantoms. Med Phys, 2018. 45(11): p. 5283-5292.
66. Tani, K., et al., MCNP simulations with a personalised voxel phantom to verify 131I content in thyroid estimated based on measurements with an NaI(Tl) spectrometer. Radiat Prot Dosimetry, 2019.
67. Villoing, D., et al., S values for neuroimaging procedures on Korean pediatric and adult head computational phantoms. Radiat Prot Dosimetry, 2019.
68. Hubbell, J.H. and S.M. Seltzer, Tables of X-ray mass attenuation coefficients and mass energy-absorption coefficients 1 keV to 20 MeV for elements Z= 1 to 92 and 48 additional substances of dosimetric interest. 1995, The National Institute of Standards and Technology.
69. DeMarco, J.J., et al., A verification of the Monte Carlo code MCNP for thick target bremsstrahlung calculations. Med Phys, 1995. 22(1): p. 11-6.
70. Sechopoulos, I., et al., Monte carlo reference data sets for imaging research. Report of the AAPM Task Group No. 195,(under review), 2014.
71. Johns, H.E., Physics of radiology. 1971: Charles River Media.
72. Hernandez, A.M., et al., Generation and analysis of clinically relevant breast imaging x-ray spectra. Med Phys, 2017. 44(6): p. 2148-2160.
73. Boone, J.M., T.R. Fewell, and R.J. Jennings, Molybdenum, rhodium, and tungsten anode spectral models using interpolating polynomials with application to mammography. Med Phys, 1997. 24(12): p. 1863-74.
74. Fedon, C., et al., Monte Carlo study on optimal breast voxel resolution for dosimetry estimates in digital breast tomosynthesis. Phys Med Biol, 2018. 64(1): p. 015003.
75. Pan, Y., et al., Development of 1-year-old computational phantom and calculation of organ doses during CT scans using Monte Carlo simulation. Phys Med Biol, 2014. 59(18): p. 5243-60.
76. Vollmar, S.V. and W.A. Kalender, Reduction of dose to the female breast in thoracic CT: a comparison of standard-protocol, bismuth-shielded, partial and tube-current-modulated CT examinations. Eur Radiol, 2008. 18(8): p. 1674-82.
77. Duan, X., et al., Dose reduction to anterior surfaces with organ-based tube-current modulation: evaluation of performance in a phantom study. AJR Am J Roentgenol, 2011. 197(3): p. 689-95.