JBiSE  Vol.8 No.8 , August 2015
Comparison of Different Reconstruction Algorithms for Decreasing the Exposure Dose during Digital Breast Tomosynthesis: A Phantom Study
Author(s) Tsutomu Gomi
ABSTRACT
We compared reconstruction algorithms [filtered back projection (FBP), maximum likelihood expectation maximization (MLEM), and the simultaneous iterative reconstruction technique (SIRT)] in terms of the radiation dose and image quality, for exploring the possibility of decreasing the radiation dose during digital breast tomosynthesis (DBT). The three algorithms were implemented using a DBT system and experimentally evaluated using measurements, such as signal difference-to-noise ratio (SDNR) and intensity profile, on a BR3D phantom (infocus plane image). The possible radiation dose reduction, contrast improvement, and artifact reduction in DBT were evaluated using different exposure levels and the three reconstruction techniques. We performed statistical analysis (one-way analysis of variance) of the SDNR data. The effectiveness of each technique for enhancing the visibility of the BR3D phantom was quantified with regard to SDNR (FBP versus MLEM, P < 0.05; FBP vs. SIRT, P < 0.05; MLEM vs. SIRT, P = 0.945); the artifact reduction was quantified with regard to the intensity profile. MLEM and SIRT produced reconstructed images with SDNR values indicative of low-contrast visibility. The SDNR value for the half-radiation dose MLEM and SIRT images was close to that of the FBP reference radiation dose image. Artifacts were decreased in the MLEM and SIRT images (in the infocus plane) according to the intensity profiles that we obtained. With MLEM and SIRT, the radiation dose may be decreased to half comparison with FBP.

Cite this paper
Gomi, T. (2015) Comparison of Different Reconstruction Algorithms for Decreasing the Exposure Dose during Digital Breast Tomosynthesis: A Phantom Study. Journal of Biomedical Science and Engineering, 8, 471-478. doi: 10.4236/jbise.2015.88044.
References
[1]   Sone, S., Kasuga, T., Sakai, F., Kawai, T., Oguchi, K., Hirano, H., Li, F., Kubo, K., Honda, T., Hniuda, M., Takemura, K. and Hosoba, M. (1995) Image Processing in the Digital Tomosynthesis for Pulmonary Imaging. European Radiology, 5, 96-101.

[2]   Skaane, P., Bandos, A.I., Gullien, R., Eben, E.B., Ekseth, U., Haakenaasen, U., Izadi, M., Jebsen, I.N., Jahr, G., Krager, M., Niklason, L.T., Hofvind, S. and Gur, D. (2013) Comparison of Digital Mammography Alone and Digital Mammography plus Tomosynthesis in a Population-Based Screening Program. Radiology, 267, 47-56.
http://dx.doi.org/10.1148/radiol.12121373

[3]   Wu, T., Stewart, A., Stanton, M., McCauley, T., Phillips, W., Kopans, D.B., Moore, R.H., Eberhard, J.W., Opsahl-Ong, B., Niklason, L. and Williams, M.B. (2003) Tomographic Mammography Using a Limited Number of Low-Dose Cone-Beam Projection Images. Medical Physics, 30, 365-380.
http://dx.doi.org/10.1118/1.1543934

[4]   Helvie, M.A., Roubidoux, M.A., Zhang, Y., Carson, P.L. and Chan, H.P. (2006) Tomosynthesis Mammography vs. Conventional Mammography: Lesion Detection and Reader Reference. Initial Experience. RSNA Program Book, 335.

[5]   Sechopoulos, I., Bliznakova, K. and Fei, B. (2013) Power Spectrum Analysis of the X-Ray Scatter Signal in Mammography and Breast Tomosynthesis Projections. Medical Physics, 40, 101905-1-101905-7.
http://dx.doi.org/10.1118/1.4820442

[6]   Gur, D., Zuley, M.L., Anello, M.I., Rathfon, G.Y., Chough, D.M., Ganott, M.A., Hakim, C.M., Wallace, L., Lu, A. and Bandos, A.I. (2012) Dose Reduction in Digital Breast Tomosynthesis (DBT) Screening Using Synthetically Reconstruction Projection Images: An Observer Performance Study. Academic Radiology, 19, 166-171.
http://dx.doi.org/10.1016/j.acra.2011.10.003

[7]   Dobbins 3rd, J.T. and Godfrey, D.J. (2003) Digital X-Ray Tomosynthesis: Current State of the Art and Clinical Potential. Physics in Medicine and Biology, 48, R65-R106.
http://dx.doi.org/10.1088/0031-9155/48/19/R01

[8]   Bleuet, P., Guillemaud, R., Magnin, I. and Desbat, L. (2001) An Adapted Fan Volume Sampling Scheme for 3D Algebraic Reconstruction in Linear Tomosynthesis. IEEE Transactions on Nuclear Science, 3, 1720-1724.

[9]   Wu, T., Zhang, J., Moore, R., Rafferty, E. and Kopans, D. (2004) Digital Tomosynthesis Mammography Using a Parallel Maximum-Likelihood Reconstruction Method. Proceedings of SPIE, 5368, 1-11.
http://dx.doi.org/10.1117/12.534446

[10]   Gomi, T. (2014) A Comparison of Reconstruction Algorithms regarding Exposure Dose Reductions during Digital Breast Tomosynthesis. Journal of Biomedical Science and Engineering, 7, 516-525.
http://dx.doi.org/10.4236/jbise.2014.78053

[11]   Gordon, R., Bender, R. and Hermen, G.T. (1970) Algebraic Reconstruction Techniques (ART) for Three-Dimensional Electron Microscopy and X-Ray Photography. Journal of Theoretical Biology, 29, 471-481.
http://dx.doi.org/10.1016/0022-5193(70)90109-8

[12]   Mathworks Inc. (2014)
http://www.mathworks.com/products/matlab/

[13]   Dance, D.R., Young, K.C. and van Engen, R.E. (2011) Estimation of Mean Glandular Dose for Breast Tomosynthesis: Factors for Use with the UK, European and IAEA Breast Dosimetry Protocols. Physics in Medicine and Biology, 56, 453-471.
http://dx.doi.org/10.1088/0031-9155/56/2/011

[14]   Li, K., Ge, Y., Garrett, J., Bevins, N., Zambelli, J. and Chen, G.H. (2014) Grating-Based Phase Contrast Tomosynthesis Imaging: Proof-of-Concept Experimental Studies. Medical Physics, 41, 011903-1-011903-11.
http://dx.doi.org/10.1118/1.4835455

[15]   Das, M., Gifford, H.C., O’Connor, J.M. and Glick, S.J. (2011) Penalized Maximum Likelihood Reconstruction for Improved Microcalcification Detection in Breast Tomosynthesis. IEEE Transactions on Medical Imaging, 30, 904-914.
http://dx.doi.org/10.1109/TMI.2010.2089694

 
 
Top