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Spatially Adaptive Spectral Denoising for MR Spectroscopic Imaging using Frequency-Phase Non-local Means

  • Dhritiman DasEmail author
  • Eduardo Coello
  • Rolf F. Schulte
  • Bjoern H. Menze
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9902)

Abstract

Magnetic resonance spectroscopic imaging (MRSI) is an imaging modality used for generating metabolic maps of the tissue in-vivo. These maps show the concentration of metabolites in the sample being investigated and their accurate quantification is important to diagnose diseases. However, the major roadblocks in accurate metabolite quantification are: low spatial resolution, long scanning times, poor signal-to-noise ratio (SNR) and the subsequent noise-sensitive non-linear model fitting. In this work, we propose a frequency-phase spectral denoising method based on the concept of non-local means (NLM) that improves the robustness of data analysis and scanning times while potentially increasing spatial resolution. We evaluate our method on simulated data sets as well as on human in-vivo MRSI data. Our denoising method improves the SNR while maintaining the spatial resolution of the spectra.

Notes

Acknowledgments

The research leading to these results has received funding from the European Union’s H2020 Framework Programme (H2020-MSCA-ITN-2014) under grant agreement no 642685 MacSeNet.

References

  1. 1.
    Vespa project (Versatile simulation, pulses and analysis). https://scion.duhs.duke.edu/vespa/project
  2. 2.
    Bottomley, P.A.: Spatial localization in NMR spectroscopy in vivo. Ann. N. Y. Acad. Sci. 508, 333–348 (1987). doi: 10.1111/j.1749-6632.1987.tb32915.xCrossRefGoogle Scholar
  3. 3.
    Buades, A., Coll, B.: A non-local algorithm for image denoising. Comput. Vis. Pattern 2(0), 60–65 (2005)Google Scholar
  4. 4.
    Collins, D.L., Zijdenbos, P., Kollokian, V., Sled, J.G., Kabani, N.J., Holmes, C.J., Evans, C.: Design and construction of a realistic digital brain phantom. IEEE Trans. Med. Imaging 17(3), 463–468 (1998)CrossRefGoogle Scholar
  5. 5.
    Coupé, P., Yger, P., Prima, S., Hellier, P., Kervrann, C.: An optimized blockwise non local means denoising filter for 3D magnetic resonance images. IEEE Trans. Med. Imaging 27(4), 425–441 (2008)CrossRefGoogle Scholar
  6. 6.
    Haase, A., Frahm, J., Hänicke, W., Matthaei, D.: 1H NMR chemical shift selective (CHESS) imaging. Phys. Med. Biol. 30(4), 341–344 (1985). http://stacks.iop.org/0031-9155/30/i=4/a=008CrossRefGoogle Scholar
  7. 7.
    Kelm, B.M., Kaster, F.O., Henning, A., Weber, M.A., Bachert, P., Boesiger, P., Hamprecht, F.A., Menze, B.H.: Using spatial prior knowledge in the spectral fitting of MRS images. NMR Biomed. 25(1), 1–13 (2012)CrossRefGoogle Scholar
  8. 8.
    Nguyen, H.M., Peng, X., Do, M.N., Liang, Z.: Spatiotemporal denoising of MR spectroscopic imaging data by low-rank approximations. IEEE ISBI: From Nano to Macro 0(3), 857–860 (2011)Google Scholar
  9. 9.
    Posse, S., DeCarli, C., Le Bihan, D.: Three-dimensional echo-planar MR spectroscopic imaging at short echo times in the human brain. Radiology 192(3), 733–738 (1994). http://pubs.rsna.org/doi/abs/10.1148/radiology.192.3.8058941CrossRefGoogle Scholar
  10. 10.
    Pouwels, P.J.W., Frahm, T.: Regional metabolite concentrations in human brain as determined by quantitative localized proton MRS. Magn. Reson. Med. 39(1), 53–60 (1998)CrossRefGoogle Scholar
  11. 11.
    Provencher, S.W.: Estimation of metabolite concentrations from localized in vivo proton NMR spectra. Magn. Reson. Med. 30(6), 672–679 (1993). http://www.ncbi.nlm.nih.gov/pubmed/8139448CrossRefGoogle Scholar
  12. 12.
    de Graaf, R.A.: In Vivo NMR Spectroscopy: Principles and Techniques, 2nd edn. Wiley, Hoboken (2013)Google Scholar
  13. 13.
    Schulte, R.F., Lange, T., Beck, J., Meier, D., Boesiger, P.: Improved two-dimensional J-resolved spectroscopy. NMR Biomed. 19(2), 264–270 (2006)CrossRefGoogle Scholar
  14. 14.
    Wang, Y., Li, S.: Differentiation of metabolic concentrations between gray matter and white matter of human brain by in vivo ’ h magnetic resonance spectroscopy. Magn. Reson. Med. 39(1), 28–33 (2005)CrossRefGoogle Scholar

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© Springer International Publishing AG 2016

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Authors and Affiliations

  • Dhritiman Das
    • 1
    • 3
    Email author
  • Eduardo Coello
    • 2
    • 3
  • Rolf F. Schulte
    • 3
  • Bjoern H. Menze
    • 1
  1. 1.Department of Computer ScienceTechnical University of MunichMunichGermany
  2. 2.Department of PhysicsTechnical University of MunichMunichGermany
  3. 3.GE Global ResearchMunichGermany

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