Advertisement

Ordered subsets Non-Local means constrained reconstruction for sparse view cone beam CT system

  • Yining HuEmail author
  • Zheng Wang
  • Lizhe XieEmail author
  • Limin Luo
Scientific Paper
  • 18 Downloads

Abstract

Sparse-view sampling scans reduce the patient's radiation dose by reducing the total exposure duration. CT reconstructions under such scan mode are often accompanied by severe artifacts due to the high ill-posedness of the problem. In this paper, we use a Non-Local means kernel as a regularization constraint to reconstruct image volumes from sparse-angle sampled cone-beam CT scans. To overcome the huge computational cost of the 3D reconstruction, we propose a sequential update scheme relying on ordered subsets in the image domain. It is shown through experiments on simulated and real data and comparisons with other methods that the proposed approach is robust enough to deal with the number of views reduced up to 1/10. When coupled with a CUDA parallel computing technique, the computation speed of the iterative reconstruction is greatly improved.

Keywords

Image reconstruction Tomography Cone beam CT Sparse view Low dose 

Notes

Acknowledgements

The authors are indebted to Dr. Jean-Louis Coatrieux, University of Rennes 1, Inserm U1099, Rennes, France, for his contributions in conducting this work. They thank Prof. Jianhua Ma from Southern Medical University, Guangzhou, China, for providing the experimental data. They also thank Prof. Jinglu Zhang from Nanjing Medical University, Nanjing, China, for her assistance in completing the experiments. This project has been supported by the National Natural Science Foundation of China under Grant No. 81530060; Basic Research Program of Jiangsu Province under Grant (BK20180670); Open Project from Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University (JSKLOD-KF-1701, JSKLOD-KF-1708); Open Project from Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China (K93-9–2014-10C); Science and Technology Plan of Nanjing (201715017).

Compliance with ethical standards

Conflict of interest

No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication.

Ethical approval

All applicable international, national, and institutional guidelines for the care and use of animals were followed.

References

  1. 1.
    Rothenberg LN, Pentlow KS (1992) Radiation dose in CT. RadioGraphics 12:1225–1243PubMedCrossRefPubMedCentralGoogle Scholar
  2. 2.
    Diederich S, Windmann R et al (1999) Pulmonary nodules: experimental and clinical studies at low-dose CT. Radiology 213:289–298PubMedCrossRefPubMedCentralGoogle Scholar
  3. 3.
    Brenner DJ, Hall EJ (2007) Computed tomography—an increasing source of radiation exposure. N Engl J Med 357(22):2277–2284CrossRefGoogle Scholar
  4. 4.
    Mannudeep K, Michael MM et al (2004) Strategies for CT radiation dose optimization. Radiology 230:619–628CrossRefGoogle Scholar
  5. 5.
    Jung K, Lee K, Kim S, Kim T, Pyeun Y, Lee J (2000) Low-dose, volumetric helical CT: image quality, radiation dose, and usefulness for evaluation of bronchiectasis. Invest Radiol 35:557–563PubMedCrossRefPubMedCentralGoogle Scholar
  6. 6.
    Hyvönen N, Kalke M, Lassas M, Setälä H, Siltanen S (2010) Three-dimensional dental X-ray imaging by combination of panoramic and projection data. Inverse Probl Imaging 4:257–271CrossRefGoogle Scholar
  7. 7.
    Mueller J, Siltanen S (2012) Linear and nonlinear inverse problems with practical applications, computational science and engineering, vol 10. SIAM, PhiladelphiaCrossRefGoogle Scholar
  8. 8.
    Donoho D (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289–1306CrossRefGoogle Scholar
  9. 9.
    E Sidky, P Xiaochuan (2006) Accurate image reconstruction in circular cone-beam computed tomography by total variation minimization: a preliminary investigation. In: Nuclear science symposium conference record, IEEE, vol. 5, IEEEGoogle Scholar
  10. 10.
    Lustig M, Donoho D, Pauly JM (2007) Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn Reson Med 58(6):1182–1195PubMedCrossRefPubMedCentralGoogle Scholar
  11. 11.
    Trzasko J, Manduca A, Borisch E (2009) Highly undersampled magnetic resonance image reconstruction via homotopic ell-0-minimization. IEEE Trans Med Imaging 28(1):106–121PubMedCrossRefPubMedCentralGoogle Scholar
  12. 12.
    Lu YJ, Zhang XQ, Douraghy A, Stout D, Tian J, Chan TF, Chatziioannou AF (2009) Source reconstruction for spectrally-resolved bioluminescence tomography with sparse a prior information. Opt Express 17(10):8062–8080PubMedPubMedCentralCrossRefGoogle Scholar
  13. 13.
    Fang LY, Li ST, McNabb RP, Nie Q, Kuo AN, Toth CA, Izatt JA, Farsiu S (2013) Fast acquisition and reconstruction of optical coherence tomography images via sparse representation. IEEE Trans Med Imaging 32(11):2034–2049PubMedPubMedCentralCrossRefGoogle Scholar
  14. 14.
    Yu HY, Wang G (2010) A soft-threshold filtering approach for reconstruction from a limited number of projections. Phys Med Biol 55(13):3905–3916PubMedPubMedCentralCrossRefGoogle Scholar
  15. 15.
    Sidky EY, Kao CM, Pan X (2006) Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT. J X-Ray Sci Technol 14(2):119–139Google Scholar
  16. 16.
    Sidky EY, Pan X (2008) Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization. Phys Med Biol 53(17):4777PubMedPubMedCentralCrossRefGoogle Scholar
  17. 17.
    Niu S, Gao Y, Bian Z et al (2014) Sparse-view x-ray CT reconstruction via total generalized variation regularization. Phys Med Biol 59(12):2997PubMedPubMedCentralCrossRefGoogle Scholar
  18. 18.
    Yu HY, Wang G (2009) Compressed sensing based interior tomography. Phys Med Biol 54(9):2791–2805PubMedPubMedCentralCrossRefGoogle Scholar
  19. 19.
    Bian J et al (2010) Evaluation of sparse-view reconstruction from flat-panel-detector cone-beam CT. Phys Med Biol 55(22):6575PubMedPubMedCentralCrossRefGoogle Scholar
  20. 20.
    Han X et al (2010) Algorithm-enabled low-dose micro-CT imaging. IEEE Trans Med Imaging 30(3):606–620PubMedPubMedCentralCrossRefGoogle Scholar
  21. 21.
    Bian J et al (2012) Optimization-based image reconstruction from sparse-view data in offset-detector CBCT. Phys Med Biol 58(2):205PubMedPubMedCentralCrossRefGoogle Scholar
  22. 22.
    Sidky EY, Chartrand R, Boone JM et al (2014) Constrained T p V minimization for enhanced exploitation of gradient sparsity: application to CT image reconstruction. IEEE J Transl Eng Health Med 2:1–18CrossRefGoogle Scholar
  23. 23.
    Chen Z, Jin X, Li L et al (2013) A limited-angle CT reconstruction method based on anisotropic TV minimization. Phys Med Biol 58(7):2119PubMedCrossRefPubMedCentralGoogle Scholar
  24. 24.
    Chen GH, Tang J, Leng S (2008) Prior image constrained compressed sensing (PICCS): a method to accurately reconstruction dynamic CT image from highly undersampled projection data sets. Med Phys 35(2):660–663PubMedPubMedCentralCrossRefGoogle Scholar
  25. 25.
    Lauzier PT, Tang J, Chen GH (2012) Prior image constrained compressed sensing: implementation and performance evaluation. Med Phys 39(1):66–80PubMedCrossRefPubMedCentralGoogle Scholar
  26. 26.
    Candes EJ, Romberg JK (2005) Signal recovery from random projections. Comput Imaging 3:76–86CrossRefGoogle Scholar
  27. 27.
    Guo WH, Yin WT (2012) Edge guided reconstruction for compressive imaging. SIAM J Imaging Sci 5(3):809–834CrossRefGoogle Scholar
  28. 28.
    Lee JMK, Bresler Y, Ye JC (2011) Compressive diffuse optical tomography: noniterative exact reconstruction using joint sparsity. IEEE Trans Med Imaging 30(5):1129–1142PubMedCrossRefPubMedCentralGoogle Scholar
  29. 29.
    Hu Y, Xie L, Luo L, Nunes JC, Toumoulin C (2011) L0 constrained sparse reconstruction for multi-slice helical CT reconstruction. Phys Med Biol 56(4):1173–1189PubMedPubMedCentralCrossRefGoogle Scholar
  30. 30.
    Zhang JF, Chen Y et al (2015) Gamma prior based reconstruction for low dose CT. Phys Med Biol 60(17):6901–6921PubMedCrossRefPubMedCentralGoogle Scholar
  31. 31.
    Zhang J, Hu Y, Yang J et al (2017) Sparse-view X-ray CT reconstruction with Gamma regularization. Neurocomputing 230:251–269CrossRefGoogle Scholar
  32. 32.
    Chen Y, Yang Z, Hu Y, Yang G, Zhu Y, Li Y et al (2012) Thoracic low-dose CT image processing using an artifact suppressed large-scale nonlocal means. Phys Med Biol 57(9):2667–2688PubMedCrossRefPubMedCentralGoogle Scholar
  33. 33.
    Hu Y, Xie L, Meng J, Yang J, Zhang L, Yang B, Chen Y, Luo L, Yin X (2015) Low-dose lung CT processing using weighted intensity averaging over large-scale neighborhoods. Australas Phys Eng Sci Med 38(2):345–356PubMedCrossRefPubMedCentralGoogle Scholar
  34. 34.
    Li Z, Yu L, Trzasko JD, Lake DS, Blezek DJ, Fletcher JG et al (2014) Adaptive nonlocal means filtering based on local noise level for CT denoising. Med Phys 41(1):011908PubMedCrossRefPubMedCentralGoogle Scholar
  35. 35.
    Zhang H, Ma J, Wang J, Liu Y, Lu H, Liang Z (2014) Statistical iterative reconstruction for low-dose CT using nonlocal means-based prior. Comput Med Imaging Graph 38(6):423–435PubMedPubMedCentralCrossRefGoogle Scholar
  36. 36.
    Hang H, Ma J, Wang J, Liu Y, Han H, Lu H, Moore W, Liang Z (2015) Statistical image reconstruction for low-dose CT using nonlocal means-based prior Part II: an adaptive approach. Comput Med Imaging Graph 43:26–35CrossRefGoogle Scholar
  37. 37.
    Li B, Lyu Q, Ma J, Wang J (2016) Iterative reconstruction for CT perfusion with a prior-image induced hybrid nonlocal means regularization. Med Phys 43(4):1688PubMedPubMedCentralCrossRefGoogle Scholar
  38. 38.
    Kim H, Chen J, Wang A et al (2016) Non-local total-variation (NLTV) minimization combined with reweighted L1-norm for compressed sensing CT reconstruction. Phys Med Biol 61(18):6878PubMedCrossRefPubMedCentralGoogle Scholar
  39. 39.
    Xie L, Hu Y, Yan B et al (2015) An effective CUDA parallelization of projection in iterative tomography reconstruction. PLoS ONE 10(11):e0142184PubMedPubMedCentralCrossRefGoogle Scholar
  40. 40.
    Thibault JB, Sauer KD, Bouman CA et al (2007) A three-dimensional statistical approach to improved image quality for multislice helical CT. Med Phys 34(11):4526–4544PubMedCrossRefPubMedCentralGoogle Scholar
  41. 41.
    Hudson HM, Larkin RS (1994) Accelerated image reconstruction using ordered subsets of projection data. IEEE Trans Med Imaging 13(4):601–609PubMedCrossRefPubMedCentralGoogle Scholar
  42. 42.
    Kamphuis C, Beekman FJ (1998) Accelerated iterative transmission CT reconstruction using an ordered subsets convex algorithm. IEEE Trans Med Imaging 17(6):1101–1105PubMedCrossRefPubMedCentralGoogle Scholar
  43. 43.
    Erdogan H, Fessler JA (1999) Ordered subsets algorithms for transmission tomography. Phys Med Biol 44(11):2835PubMedCrossRefPubMedCentralGoogle Scholar
  44. 44.
    Kim D, Pal D, Thibault JB et al (2013) Accelerating ordered subsets image reconstruction for X-ray CT using spatially nonuniform optimization transfer. IEEE Trans Med Imaging 32(11):1965–1978PubMedCrossRefPubMedCentralGoogle Scholar
  45. 45.
    Kim D, Ramani S, Fessler JA (2015) Combining ordered subsets and momentum for accelerated X-ray CT image reconstruction. IEEE Trans Med Imaging 34(1):167–178PubMedCrossRefPubMedCentralGoogle Scholar
  46. 46.
    Wang Z, Bovik AC, Sheikh HR et al (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612PubMedCrossRefPubMedCentralGoogle Scholar
  47. 47.
    Hu Y, Xie L, Meng J et al (2015) Low-dose lung CT processing using weighted intensity averaging over large-scale neighborhoods. Australas Phys Eng Sci Med 38(2):345–356PubMedCrossRefPubMedCentralGoogle Scholar
  48. 48.
    Chen Y, Shi L, Feng Q et al (2014) Artifact suppressed dictionary learning for low-dose CT image processing. IEEE Trans Med Imaging 33(12):2271–2292PubMedCrossRefPubMedCentralGoogle Scholar
  49. 49.
    Chen H, Zhang Y, Kalra MK et al (2017) Low-dose CT with a residual encoder-decoder convolutional neural network. IEEE Trans Med Imaging 36(12):2524–2535PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© Australasian College of Physical Scientists and Engineers in Medicine 2019

Authors and Affiliations

  1. 1.School of Cyber Science and EngineeringSoutheast UniversityNanjingChina
  2. 2.Key Laboratory of Computer Network and Information Integration (Southeast University)Ministry of EducationNanjingChina
  3. 3.Institute of StomatologyNanjing Medical UniversityNanjingChina
  4. 4.Jiangsu Key Laboratory of Oral DiseasesNanjing Medical UniversityNanjingChina
  5. 5.Centre de Recherche en Information BiomédicaleSino-Francais (LIA CRIBs)RennesFrance

Personalised recommendations