Evaluating the simulation of radiation dose reduction in a digital breast tomosynthesis system featuring an amorphous silicon (a-Si) detector

Palavras-chave: Tomossíntese digital mamária, redução da dose de radiação, ruído quântico, correlação do ruído, detector de raios X.


The validation of many dose optimization methods in x-ray imaging requires clinical images from a range of signal-to-ratio regimes. This data is commonly generated through computer simulation. For this purpose, our group developed a method to simulate dose reduction for digital breast tomosynthesis. In the previous work, tests were performed in a system that features an amorphous selenium detector with minimal pixel correlation. In the current work, we evaluate the simulation performance in an amorphous silicon system, which yields a relevant pixel correlation. Signal and noise characteristics in real and simulated images were measured using the signal-to-noise ratio (SNR) and the normalized noise power spectrum (NNPS). The simulation method assessment was performed through the average relative error between simulated and real images. The SNR results point to an error of less than 2.5% between the images. The noise correlation influence was verified through the NNPS. The tests pointed to errors up to 55% between the real and simulated images when the correlation kernel is not considered, whereas the error considering the correlation kernel was kept around 5.5%. Therefore, the results show that the correlation kernel is a relevant factor to be considered when simulating amorphous silicon systems.


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Inca.gov.br [homepage on the internet]. Brazil: Instituto Nacional de Câncer José Alencar Gomesda Silva (INCA); [Accessed 2019 April 15]. Available from: www.inca.gov.br/.

Blanks RG, Wallis MG, Alison R, Kearins O, Jenkins J,Patnick J, et al. Impact of digital mammography on cancer detection and recall rates: 11.3 million screening episodes in the English National Health Service Breast Cancer Screening Program. Radiology. 2018; 290(3): 629-637. https://doi.org/10.1148/radiol.2018181426

Saadatmand S, Bretveld R, Siesling S, Tilanus-Linthorst MM. Influence of tumour stage at breast cancer detection on survival in modern times: population based study in 173 797 patients. Bmj. 2015; 351:h4901. https://doi.org/10.1136/bmj.h4901

Vedantham S, Karellas A, Vijayaraghavan GR, Kopans DB. Digital breast tomosynthesis: state of the art. Radiology.2015;277(3):663–684. https://doi.org/10.1148/radiol.2015141303

Michell M, Batohi B. Role of tomosynthesis in breast imaging going forward. Clinical radiology.2018;73(4):358–371.

Samei E, Saunders Jr RS, Baker JA, Delong DM. Digital mammography: effects of reduced radiation dose on diagnostic performance. Radiology. 2007;243(2):396–404. https://doi.org/10.1016/j.crad.2018.01.001

Ruschin M, Timberg P, Båth M, Hemdal B, Svahn T,Saunders RS, et al. Dose dependence of mass and microcalcification detection in digital mammography: free response human observer studies. Medical physics.2007;34(2):400–407. https://doi.org/10.1118/1.2405324

Saunders RS, Baker JA, Delong DM, Johnson JP, Samei E. Does image quality matter? Impact of resolution and noise on mammographic task performance. Medical physics. 2007;34(10):3971–3981. https://doi.org/10.1118/1.2776253

Hadjipanteli A, Elangovan P, Mackenzie A, Looney PT,Wells K, Dance DR, et al. The effect of system geometry and dose on the threshold detectable calcification diameter in 2D-mammography and digital breast tomosynthesis. Physics in Medicine & Biology. 2017;62(3):858-877. https://doi.org/10.1088/1361-6560/aa4f6e

Mackenzie A, Warren LM, Wallis MG, Given-Wilson RM,Cooke J, Dance DR, et al. The relationship between cancer detection in mammography and image quality measurements. Physica Medica. 2016;32(4):568–574. https://doi.org/10.1016/j.ejmp.2016.03.004

Timberg P, Dustler M, Petersson H, Tingberg A, Zackrisson S. Detection of calcification clusters in digital breast tomosynthesis slices at different dose levels utilizing a SRSAR reconstruction and JAFROC. In: Proceedings of SPIE Medical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment. International Society for Optics and Photonics; 2015; 9416(04). https://doi.org/10.1117/12.2081879

Reiser I, Nishikawa R. Task-based assessment of breast tomosynthesis: Effect of acquisition parameters and quantum noise. Medical physics. 2010;37(4):1591–1600. https://doi.org/10.1118/1.3357288

Yaffe MJ, Mainprize JG. Risk of radiation-induced breast cancer from mammographic screening. Radiology. 2011;258(1):98–105. https://doi.org/10.1148/radiol.10100655

Båth M, Håkansson M, Tingberg A, Månsson LG. Method of simulating dose reduction for digital radiographic systems. Radiation protection dosimetry. 2005;114(1-3):253–259. https://doi.org/10.1093/rpd/nch540

Svalkvist A, Båth M. Simulation of dose reduction in tomosynthesis. Medical physics. 2010;37(1):258–269. https://doi.org/10.1118/1.3273064

Mackenzie A, Dance DR, Workman A, Yip M, Wells K, Young KC. Conversion of mammographic images to appear with the noise and sharpness characteristics of a different detector and x-ray system. Medical physics. 2012;39(5):2721–2734. https://doi.org/10.1118/1.4704525

Mackenzie A, Dance DR, Diaz O, Young KC. Image simulation and a model of noise power spectra across a range of mammographic beam qualities. Medical physics. 2014;41(12):121901. https://doi.org/10.1118/1.4900819

Borges LR, de Oliveira HC, Nunes PF, Bakic PR, Maidment AD, Vieira MA. Method for simulating dose reduction in digital mammography using the Anscombe transformation. Medical physics. 2016;43(6Part1):2704–2714. https://doi.org/10.1118/1.4948502

Borges LR, Guerrero I, Bakic PR, Foi A, Maidment AD, Vieira MA. Method for simulating dose reduction in digital breast tomosynthesis. IEEE transactions on medical imaging. 2017;36(11):2331–2342. https://doi.org/10.1109/TMI.2017.2715826

Zheng J, Fessler JA, Chan HP. Detector blur and correlated noise modeling for digital breast tomosynthesis reconstruction. IEEE transactions on medical imaging.2018;37(1):116–127. https://doi.org/10.1109/TMI.2017.2732824

McEntee MF. Clinical Radiographic Units. In: Russo P, editor. Handbook of X-ray Imaging: Physics and Technology.1st ed. Boca Raton: CRC Press; 2017. p. 518 –544.

Azzari L, Borges LR, Foi A. Modeling and Estimation of Signal-Dependent and Correlated Noise. In: Bertalmío M, editor. Denoising of Photographic Images and Video: Fundamentals, Open Challenges and New Trends. Switzerland: Springer; 2018. p. 13–36.

Borges LR, da Costa Vieira MA, Foi A. Unbiased injection of signal-dependent noise in variance stabilized range. IEEE Signal Processing Letters.2016;23(10):1494–1498. https://doi.org/10.1109/LSP.2016.2601689

Borges LR, Barufaldi B, Caron RF, Bakic PR, Foi A, Maidment ADA, et al. Technical Note: Noise models for virtual clinical trials of digital breast tomosynthesis. Medical Physics. 2019;46(6): 2683-2689. https://doi.org/10.1002/mp.13534

Como Citar
Brandão, R. de F., Vimieiro, R. de B., Borges, L. R., Caron, R. F., & Vieira, M. A. C. (2019). Evaluating the simulation of radiation dose reduction in a digital breast tomosynthesis system featuring an amorphous silicon (a-Si) detector. Revista Brasileira De Física Médica, 13(2), 30-34. https://doi.org/10.29384/rbfm.2019.v13.n2.p30-34
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