Skip to main content
SpringerLink
Log in

Acceleration of MRI analysis using multicore and manycore paradigms

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Magnetic resonance imaging (MRI) of the brain is a safe and painless test that uses a magnetic field and radio waves to produce detailed images of the brain. FreeSurfer is a tool neuroscientists use to create models of structures in the brain. An average MRI analysis using FreeSurfer takes around 7 h on a central processing unit with 4 cores. Since execution time is so high, researchers are working on different ways to parallelize the software. Most efforts are concentrated on parallelization using multicore, specifically with OpenMP (an implementation of multithreading) reducing execution time around 20%. In this paper, we further accelerate the analysis time for FreeSurfer using the manycore processors, special multicore processors containing from dozens to thousands simpler independent cores. Specifically, we will use graphics processing unit (GPU) a manycore with thousands of simpler cores. Multicore and manycore using GPU acceleration are not mutually exclusive (we will call it GPU acceleration from now on), and we present an implementation that uses both types of accelerations (multicore and GPU). Results show that execution times using both accelerations reduce the analysis time by 70%. Manycore processors are specialist multicore processors designed for a high degree of parallel processing, containing numerous simpler, independent processor cores (from a few tens of cores to thousands or more). Manycore processors are used extensively in embedded computers and high-performance computing.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. Results obtained by upgrading GPU code from CUDA 5.0 to CUDA 9.0.

References

  1. Chen Y, Almarzouqi SJ, Morgan ML, Lee AG (2018) T1-weighted image, Springer, Berlin, pp 1747–1750. https://doi.org/10.1007/978-3-540-69000-9_1228

  2. Dagum L, Menon R (1998) Openmp: an industry standard api for shared-memory programming. IEEE Comput Sci Eng 5(1):46–55. https://doi.org/10.1109/99.660313

    Article  Google Scholar 

  3. Dale AM, Fischl B, Sereno MI (1999) Cortical surface-based analysis: I. Segmentation and surface reconstruction. NeuroImage 9(2):179–194. https://doi.org/10.1006/nimg.1998.0395

    Article  Google Scholar 

  4. Delgado J, Moure J, Vives-Gilabert Y, Delfino M, Espinosa A, Gomez-Anson B (2014) Improving the execution performance of freesurfer: a new scheduled pipeline scheme for optimizing the use of CPU and GPU resources. Neuroinformatics. https://doi.org/10.1007/s12021-013-9214-1

    Article  Google Scholar 

  5. Docs G (2019) Enabling CUDA in freesurfer. https://docs.google.com/document/d/1VF_HZZ89wimZVBLkkTZ-lAL1if9gK9BKjNKhdAzjjtA/

  6. Fischl B (2012) Freesurfer. NeuroImage 62(2):774–781. https://doi.org/10.1016/j.neuroimage.2012.01.021

    Article  Google Scholar 

  7. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, van der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM (2002) Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33(3):341–355. https://doi.org/10.1016/S0896-6273(02)00569-X

    Article  Google Scholar 

  8. Fischl B, Sereno MI, Tootell RB, Dale AM (1999) High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum Brain Map 8(2):272–284. 10.1002/(sici)1097-0193(1999)8:4\(<\)272::aid-hbm10\(>\)3.0.co;2-4

  9. Fujii Y, Azumi T, Nishio N, Kato S, Edahiro M (2013) Data transfer matters for gpu computing. In: International Conference on Parallel and Distributed Systems, pp 275–282. https://doi.org/10.1109/ICPADS.2013.47

  10. GitHub I (2019) Freesurfer. In: An open-source software suite for processing human brain MRI. https://github.com/freesurfer/freesurfer

  11. Gorgolewski KJ, Auer T, Calhoun VD, Cameron Craddock R, Das S, Duff EP, Flandin G, Ghosh SS, Glatard T, Halchenko YO, Handwerker DA, Hanke M, Keator D, Li X, Michael Z, Maumet C, Nichols BN, Nichols TE, Pellman J, Poline JB, Rokem A, Schaefer G, Sochat V, Triplett W, Turner JA, Varoquaux G, Poldrack RA (2016) The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data. https://doi.org/10.1038/sdata.2016.44

    Article  Google Scholar 

  12. Iglesias JE, Leemput KV, Bhatt P, Casillas C, Dutt S, Schuff N, Truran-Sacrey D, Boxer A, Fischl B (2015) Bayesian segmentation of brainstem structures in mri. NeuroImage 113:184–195. https://doi.org/10.1016/j.neuroimage.2015.02.065

    Article  Google Scholar 

  13. (Intel) MT (2019) Intel \(\copyright \) vtune ™amplifier release notes and new features. https://software.intel.com/en-us/articles/intel-vtune-amplifier-release-notes

  14. Madhyastha TM, Koh N, Day TKM, Hernandez-Fernandez M, Kelley A, Peterson DJ, Rajan S, Woelfer KA, Wolf J, Grabowski TJ (2017) Running neuroimaging applications on amazon web services: How, when, and at what cost? Front Neuroinf 11:63. https://doi.org/10.3389/fninf.2017.00063

    Article  Google Scholar 

  15. Mcrobbie D, Moore E, Graves M, Prince M (2006) MRI from picture to proton. Cambridge: Cambridge University Press, pp 1–397. https://doi.org/10.1017/CBO9780511545405

  16. Membarth R, Lupp JH, Hannig F, Teich J, Korner M, Eckert W (2012) Dynamic task-scheduling and resource management for GPU accelerators in medical imaging. In: Architecture of computing systems—ARCS 2012, pp 147–159. Springer, Heidelberg. https://doi.org/10.1007/978-3-642-28293-5_13

  17. Nesvaag R, Bergmann O, Rimol LM, Lange EH, Haukvik UK, Hartberg CB, Fagerberg T, Soderman E, Jonsson EG, Agartz I (2012) A 5-year follow-up study of brain cortical and subcortical abnormalities in a schizophrenia cohort. Schizophr Res 142(1):209–216. https://doi.org/10.1016/j.schres.2012.10.004

    Article  Google Scholar 

  18. Nickolls J, Buck I, Garland M, Skadron K (2008) Scalable parallel programming with cuda. Queue GPU Comput 6(2):40–53. https://doi.org/10.1145/1365490.1365500

    Article  Google Scholar 

  19. Postelnicu G, Zöllei L, Fischl B (2009) Combined volumetric and surface registration. IEEE Trans Med Imaging 28(4):508–522. https://doi.org/10.1109/TMI.2008.2004426

    Article  Google Scholar 

  20. Reuter M, Schmansky NJ, Rosas HD, Fischl B (2012) Within-subject template estimation for unbiased longitudinal image analysis. NeuroImage 61(4):1402–1418. https://doi.org/10.1016/j.neuroimage.2012.02.084

    Article  Google Scholar 

  21. Wachinger C, Nho K, Saykin AJ, Reuter M, Rieckmann A (2018) A longitudinal imaging genetics study of neuroanatomical asymmetry in Alzheimer’s disease. Biol Psychiatry 84(7):522–530. https://doi.org/10.1016/j.biopsych.2018.04.017

    Article  Google Scholar 

  22. Winkler AM, Greve DN, Bjuland KJ, Nichols TE, Sabuncu MR, Haberg AK, Skranes J, Rimol LM (2017) Joint analysis of cortical area and thickness as a replacement for the analysis of the volume of the cerebral cortex. Cereb Cortex 28(2):738–749. https://doi.org/10.1093/cercor/bhx308

    Article  Google Scholar 

Download references

Acknowledgements

This work has been funded by the Regional Government of Castilla-La Mancha under the reference project SBPLY/17/180501/000353, as well as by the Ministry of Science, Innovation and Universities of the Government of Spain and the European Development Fund Regional FEDER under the reference project RTI2018-098156-B-C52.

Author information

Authors and Affiliations

  1. California Polytechnic State University, San Luis Obispo, USA

    Maria Pantoja & Maxence Weyrich

  2. Universidad de Castilla-La Mancha, Albacete, Spain

    Gerardo Fernández-Escribano

Authors
  1. Maria Pantoja
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maxence Weyrich
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gerardo Fernández-Escribano
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maria Pantoja.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pantoja, M., Weyrich, M. & Fernández-Escribano, G. Acceleration of MRI analysis using multicore and manycore paradigms. J Supercomput 76, 8679–8690 (2020). https://doi.org/10.1007/s11227-020-03154-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-020-03154-9

Keywords

Search

Navigation