An adaptive multiscale anisotropic diffusion regularized image reconstruction method for digital breast tomosynthesis

  • Yangchuan Liu
  • Cishen Zhang
  • Wenru Li
  • Yuguo Tang
  • Xin GaoEmail author
Scientific Paper


As a special case of tomography, digital breast tomosynthesis (DBT) can realize quasi-3D image reconstruction for breast lesion detection from few-view and limited-angle projection data. For DBT image reconstruction, iterative algorithms are needed to suppress artifacts due to undersampling, and adaptive regularizations are necessary for preserving the edges of masses and calcifications. This paper presents a novel reconstruction method by regularizing projection onto convex sets (POCS) with multiscale Tikhonov-total variation (MTTV). The regularization, known as adaptive multiscale anisotropic diffusion, is able to preserve edges to a considerable extent and selectively suppress noise without introducing artifacts. The proposed method is referred to as MTTV–POCS and is evaluated quantitatively using 3D numerical breast and Shepp-Logan phantoms as well as two clinical volume images acquired from an advanced DBT machine. Experimental results show that the proposed method has better performance in metrics of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) over two existing methods: adaptive-steepest-descent-POCS (ASD-POCS) and selective-diffusion regularized simultaneous algebraic reconstruction technique (SD-SART). As indicated by the results, the proposed method is applicable to DBT for high-quality image reconstruction.


Tomosynthesis Image reconstruction Anisotropic diffusion MTTV SART 



This research is supported by National Natural Science Foundation of China [61801475] and Science and Technology Plan Projects of Jiangsu – Society Development Project [BE2017671], and by Jiangsu Planned Projects for Postdoctoral Research Funds [2018K180C].

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.


  1. 1.
    Gilbert FJ, Tucker L, Young KC (2016) Digital breast tomosynthesis (DBT): a review of the evidence for use as a screening tool. Clin Radiol 71(2):141–150CrossRefGoogle Scholar
  2. 2.
    Gomi T, Sakai R, Goto M, Watanabe Y, Takeda T, Umeda T (2016) Comparison of reconstruction algorithms for decreasing the exposure dose during digital tomosynthesis for arthroplasty: a phantom study. J Digit Imaging 29(4):488–495CrossRefGoogle Scholar
  3. 3.
    Sujlana PS, Mahesh M, Vedantham S, Harvey SC, Mullen LA, Woods RW (2018) Digital breast tomosynthesis: image acquisition principles and artifacts. Clin Imaging. CrossRefPubMedGoogle Scholar
  4. 4.
    Park Y, Cho H, Je U, Hong D, Lee M, Park C, Cho H, Choi S, Koo Y (2014) Compressed-sensing (CS)-based digital breast tomosynthesis (DBT) reconstruction for low-dose, accurate 3D breast X-ray imaging. J Korean Phys Soc 65(4):565–571. doiCrossRefGoogle Scholar
  5. 5.
    Mota AM, Matela N, Oliveira NG, Almeida P (2015) Total variation minimization filter for DBT imaging. Med Phys 42(6):2827–2836. CrossRefPubMedGoogle Scholar
  6. 6.
    Sidky EY, Pan X, Reiser IS, Nishikawa RM, Moore RH, Kopans DB (2009) Enhanced imaging of microcalcifications in digital breast tomosynthesis through improved image-reconstruction algorithms. Med Phys 36(11):4920–4932. CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Lu Y, Chan H-P, Wei J, Hadjiiski LM (2010) Selective-diffusion regularization for enhancement of microcalcifications in digital breast tomosynthesis reconstruction. Med Phys 37(11):6003–6014. CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Lu Y, Chan H-P, Fessler JA, Hadjiiski L, Wei J, Goodsitt MM (2011) Adaptive diffusion regularization for enhancement of microcalcifications in digital breast tomosynthesis (DBT) reconstruction. In: 2011 SPIE Medical Imaging Conference, Florida, 17 February 2011. International Society for Optics and Photonics. doi:
  9. 9.
    Surya Prasath VB, Vorotnikov D, Pelapur R, Jose S, Seetharaman G, Palaniappan K (2015) Multiscale Tikhonov-total variation image restoration using spatially varying edge coherence exponent. IEEE Trans Image Process 24(12):5220–5235. CrossRefGoogle Scholar
  10. 10.
    De Man B, Basu S (2002) Distance-driven projection and backprojection. Paper presented at the 2002 IEEE Nuclear Science Symposium and Medical Imaging Conference, VirginiaGoogle Scholar
  11. 11.
    De Man B, Basu S (2004) Distance-driven projection and backprojection in three dimensions. Phys Med Biol 49(11):2463–2475CrossRefGoogle Scholar
  12. 12.
    Herman G (1980) Image reconstruction from projection: the fundamental of computerized projections. Academic Press, OrlandoGoogle Scholar
  13. 13.
    Peters TM (1981) Algorithms for fast back- and re-projection in computed tomography. IEEE Trans Nucl Sci 28(4):3641–3647. CrossRefGoogle Scholar
  14. 14.
    Zhuang W, Gopal SS, Hebert TJ (1994) Numerical evaluation of methods for computing tomographic projections. IEEE Trans Nucl Sci 41(4):1660–1665. CrossRefGoogle Scholar
  15. 15.
    Siddon RL (1985) Fast calculation of the exact radiological path for a three-dimensional CT array. Med Phys 12(2):252–255. CrossRefPubMedGoogle Scholar
  16. 16.
    Zeng GL, Gullberg G (1994) Ray-driven backprojector for backprojection filtering and filtered backprojection algorithms. Paper presented at the 1993 IEEE Nuclear Science Symposium and Medical Imaging Conference, CaliforniaGoogle Scholar
  17. 17.
    Jiang M, Wang G (2003) Convergence of the simultaneous algebraic reconstruction technique (SART). IEEE T Image Process 12(8):957–961. CrossRefGoogle Scholar
  18. 18.
    Zhang Y, Chan H-P, Sahiner B, Wei J, Goodsitt MM, Hadjiiski LM, Ge J, Zhou C (2006) A comparative study of limited-angle cone-beam reconstruction methods for breast tomosynthesis. Med Phys 33(10):3781–3795CrossRefGoogle Scholar
  19. 19.
    Sidky EY, Pan X (2008) Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization. Phys Med Biol 53(17):4777. CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612. CrossRefPubMedGoogle Scholar
  21. 21.
    Reiser I, Sidky EY, Nishikawa RM, Pan X (2006) Development of an analytic breast phantom for quantitative comparison of reconstruction algorithms for digital breast tomosynthesis. Paper presented at the 8th International Workshop on Digital Mammography, ManchesterCrossRefGoogle Scholar
  22. 22.
    Toft PA, Sørensen JA (1996) The Radon transform-theory and implementation. Technical University of DenmarkDanmarks Tekniske Universitet, Department of Informatics and Mathematical ModelingInstitut for Informatik og Matematisk ModelleringGoogle Scholar
  23. 23.
    Sechopoulos I (2013) A review of breast tomosynthesis. Part II. Image reconstruction, processing and analysis, and advanced applications. Med Phys 40(1):014302. CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Yan H, Dai JR (2016) A software tool of digital tomosynthesis application for patient positioning in radiotherapy. J Appl Clin Med Phys 17(2):174–193. CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Park JC, Park SH, Kim JS, Han Y, Cho MK, Kim HK, Liu ZW, Jiang SB, Song BY, Song WY (2011) Ultra-fast digital tomosynthesis reconstruction using general-purpose GPU programming for image-guided radiation therapy. Technol Cancer Res Treat 10(4):295–306CrossRefGoogle Scholar
  26. 26.
    Malet A, Pinto DG, Fernandez J, Martí R, Díaz O (2018) Breast tomosynthesis reconstruction using TIGRE software tool. In: 14th International Workshop on Breast Imaging (IWBI 2018). International Society for Optics and PhotonicsGoogle Scholar
  27. 27.
    Choi YK, Cong J (2016) Acceleration of EM-based 3D CT reconstruction using FPGA. IEEE Trans Biomed Circuit S 10(3):754–767CrossRefGoogle Scholar
  28. 28.
    Martelli M, Gac N, Mérigot A, Enderli C (2018) 3D Tomography back-projection parallelization on Intel FPGAs using OpenCL. J Signal Process Syst. CrossRefGoogle Scholar
  29. 29.
    Serrano E, Blas JG, Carretero J (2015) A comparative study of an X-ray tomography reconstruction algorithm in accelerated and cloud computing systems. Concurr Comput 27(18):5538–5556CrossRefGoogle Scholar
  30. 30.
    Chard R, Madduri R, Karonis NT, Chard K, Duffin KL, Ordonez CE, Uram TD, Fleischauer J, Foster IT, Papka ME (2018) Scalable pCT image reconstruction delivered as a cloud service. IEEE Trans Cloud Comput 6(1):182–195CrossRefGoogle Scholar

Copyright information

© Australasian College of Physical Scientists and Engineers in Medicine 2018

Authors and Affiliations

  1. 1.Medical Imaging Department, Suzhou Institute of Biomedical Engineering and TechnologyChinese Academy of SciencesSuzhouChina
  2. 2.School of Software and Electrical EngineeringSwinburne University of TechnologyHawthornAustralia
  3. 3.Department of RadiologySixth Affiliated Hospital of Sun Yat-sen UniversityGuangzhouChina

Personalised recommendations