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Journal of Real-Time Image Processing

, Volume 13, Issue 1, pp 193–204 | Cite as

Real-time patch-based medical image modality propagation by GPU computing

  • Eduardo Alcaín
  • Angel Torrado-Carvajal
  • Antonio S. Montemayor
  • Norberto Malpica
Special Issue Paper
  • 323 Downloads

Abstract

The synthesis of patient data of a certain medical image modality by applying an image processing pipeline starting from other modality is receiving a lot of interest recently, as it allows to save acquisition time and sometimes avoid radiation to the patient. An example of this is the creation of computerized tomography volumes from magnetic resonance imaging data, which can be useful for several applications such as electromagnetic simulations, cranial morphometry and attenuation correction in PET/MR systems. We present a fast patch-based algorithm for this purpose, implemented using graphics processing unit computing techniques and gaining up to \(\times\)15.9 of speedup against a multicore CPU solution and up to about \(\times\)75 against a single core CPU solution.

Keywords

Image registration CT-synthesis Patch-based approach GPU computing Multicore computation 

Notes

Acknowledgments

This research has been partially supported by the Spanish Government Projects Refs. TIN2015-69542-C2-1-R and TE2012-39095, and the NVIDIA GPU Education Center Program.

References

  1. 1.
    Asanovic, K., Bodik, R., Catanzaro, B.C., Gebis, J.J., Husbands, P., Keutzer, K., Patterson, D.A., Plishker, W.L., Shalf, J., Williams, S.W., Yelick, K.A.: The landscape of parallel computing research: a view from Berkeley. Tech. Report No. UCB/EECS-2006-183 (2006)Google Scholar
  2. 2.
    Bai, W., Shi, W., O’Regan, D.P., Tong, T., Wang, H., Jamil-Copley, S., Peters, N.S., Rueckert, D.: A probabilistic patch-based label fusion model for multi-atlas segmentation with registration refinement: application to cardiac MR images. IEEE Trans. Med. Imaging 32(7), 1302–1315 (2013)CrossRefGoogle Scholar
  3. 3.
    Barlas, G.: Multicore and GPU Programming. Elsevier, Morgan-Kaufmann, USA (2014)Google Scholar
  4. 4.
    Buades, A., Coll, B., Morel, J.M.: A review of image denoising algorithms, with a new one. Multiscale Model. Simul. 4(2), 490–530 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Burgos, N., Cardoso, J.M., Thielemans, K., Modat, M., Pedemonte, S., Dickson, J., Barnes, A., Ahmed, R., Mahoney, C.J., Schott, J.M., Duncan, J.S., Atkinson, D., Arridge, S.R., Hutton, B.F., Ourselin, S.: Attenuation correction synthesis for hybrid PET-MR scanners: application to brain studies. IEEE Trans. Med. Imaging 33(12), 2332–2341 (2014)CrossRefGoogle Scholar
  6. 6.
    Coupé, P., Manjón, J.V., Fonov, V., Pruessner, J., Robles, M., Collins, D.L.: Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation. Neuroimage 54(2), 940–954 (2011)CrossRefGoogle Scholar
  7. 7.
    Eklund, A., Dufort, P., Forsberg, D., Laconte, S.M.: Medical image processing on the GPU: past, present and future. Med. Image Anal. 17, 1073–1094 (2013)CrossRefGoogle Scholar
  8. 8.
    Flynn, M.: Very high-speed computing systems. Proc. IEEE 54, 1901–1909 (1966)CrossRefGoogle Scholar
  9. 9.
    Herraiz, J.L., España, S., Cabido, R., Montemayor, A.S., Desco, M., Vaquero, J.J., Udias, J.M.: GPU-based fast iterative reconstruction of fully 3D PET sinograms. IEEE Trans. Nucl. Sci. (TNS) 58(5), 2257–2263 (2011)Google Scholar
  10. 10.
    Homann, H., Graesslin, I., Eggers, H., Nehrke, K., Vernickel, P., Katscher, U., Dössel, O., Börnert, P.: Local SAR management by RF shimming: a simulation study with multiple human body models. Magn. Reson. Mater. Phys. Biol. Med. 25(3), 193–204 (2012)CrossRefGoogle Scholar
  11. 11.
    Iglesias, J.E., Sabuncu, M.R.: Multi-atlas segmentation of biomedical images: a survey. Med. Image Anal. 24(1), 205–219 (2015)CrossRefGoogle Scholar
  12. 12.
    Katkovnik, V., Foi, A., Egiazarian, K., Astola, J.: From local kernel to nonlocal multiple-model image denoising. Int. J. Comput. Vis. 86(1), 1–32 (2010)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Khronos OpenCL Working Group (2013) The OpenCL specification 2.0Google Scholar
  14. 14.
    Kroon, D.J., Slump, C.: MRI modality transformation in Demon registration. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI’09), pp. 963–966 (2009)Google Scholar
  15. 15.
    NVIDIA Corp. (2015) CUDA C programming guide v.7.5Google Scholar
  16. 16.
    OpenMP Architecture Review Board (2013) OpenMP specification 4.0. http://openmp.org/. Accessed 10 Mar 2015
  17. 17.
    Rousseau, F., Habas, P.A., Studholme, C.: A supervised patch-based approach for human brain labeling. IEEE Trans. Med. Imaging 30(10), 1852–1862 (2011)CrossRefGoogle Scholar
  18. 18.
    Rueda, A., Malpica, N., Romero, E.: Single-image super-resolution of brain MR images using overcomplete dictionaries. Med. Image Anal. 17(1), 113–132 (2013)CrossRefGoogle Scholar
  19. 19.
    Shi, L., Liu, W., Zhang, H., Xie, Y., Wang, D.: A survey of GPU-based medical image computing techniques. Quant. Imaging Med. Surg. 2, 188–206 (2012)Google Scholar
  20. 20.
    Smistad, E., Falch, T.L., Bozorgi, M., Elster, A.C., Lindseth, F.: Medical image segmentation on GPUs—a comprehensive review. Med. Image Anal. 20(1), 1–18 (2015)CrossRefGoogle Scholar
  21. 21.
    Torrado-Carvajal, A., Hernandez-Tamames, J.A., Herraiz, J.L., Eryaman, Y., Rozenholc, Y., Adalsteinsson, E., Wald, L.L., Malpica, N.: A multi-atlas and label fusion approach for patient-specific MRI based segmentation. In: Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM), vol. 22, p. 1177 (2014)Google Scholar
  22. 22.
    Wilt, N.: The CUDA Handbook: A Comprehensive Guide to GPU Programming. Pearson Education, USA (2013)Google Scholar
  23. 23.
    Wagenknecht, G., Kaiser, H.J., Mottaghy, F.M., Herzog, H.: MRI for attenuation correction in PET: methods and challenges. Magn. Reson. Mater. Phys. Biol. Med. 26(1), 99–113 (2013)CrossRefGoogle Scholar
  24. 24.
    Wang, Z., Donoghue, C., Rueckert, D.: Patch-based segmentation without registration: application to knee MRI. In: 4th International Workshop on Machine Learning in Medical Imaging, pp. 98–105 (2013)Google Scholar
  25. 25.
    Ye, D.H., Zikic, D., Gloker, B., Criminisi, A., Konukoglu, E.: Modality propagation: coherent synthesis of subject-specific scans with data-driven regularization. Lect. Notes Comput. Sci. 8149, 606–613 (2013)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Eduardo Alcaín
    • 1
  • Angel Torrado-Carvajal
    • 1
  • Antonio S. Montemayor
    • 1
  • Norberto Malpica
    • 1
  1. 1.MóstolesSpain

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