Skip to main content

Advertisement

Log in

Exact quantification of time signals from magnetic resonance spectroscopy by the fast Padé transform with applications to breast cancer diagnostics

  • Original Paper
  • Published:
Journal of Mathematical Chemistry Aims and scope Submit manuscript

Abstract

Mathematical modeling via the fast Padé transform (FPT) is applied according to experimental NMR data encoded from (a) normal, non-infiltrated breast tissue, (b) benign pathology (fibroadenoma) and (c) malignant breast tissue. At a partial signal length N P  = 1500, the FPT provided exact reconstruction of all the input spectral parameters for the time signals corresponding to the normal, benign as well as to the malignant lesions. The converged parametric results remained stable at longer signal lengths. The Padé absorption spectra yielded unequivocal resolution of all the extracted physical metabolites, even of those that were nearly completely overlapping (phosphocholine and phosphoethanolamine at 3.22 ppm). The capacity of the FPT to resolve and precisely quantify the physical resonances as encountered in normal versus benign versus malignant breast is demonstrated. In particular, the FPT unambiguously delineated and quantified diagnostically important metabolites such as lactate, as well as choline, phosphocholine and glycerophosphocholine that are very closely overlapping and may represent MR-retrievable molecular markers of breast cancer. This was achieved by the FPT without any fitting or numerical integration of peak areas. We conclude that these advantages of the FPT could be of definite benefit for breast cancer diagnostics via NMR and that this line of investigation should continue with encoded data from benign and malignant breast tissue, in vitro and in vivo. We anticipate that Padé-optimized MRS will reduce the false positive rates of MR-based modalities and further improve their sensitivity. Once this is achieved, and given that MR entails no ionizing radiation, new possibilities for screening/early detection open up, especially for risk groups, e.g. Padé-optimized MRS could be used with greater surveillance frequency among younger women with high breast cancer risk.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

Abbreviations

Ala:

Alanine

au:

Arbitrary units

β-Glc:

Beta-glucose

CDP:

Cytosine diphosphate

Cho:

Choline

FID:

Free induction decay

FFT:

Fast Fourier transform

FPT:

Fast Padé transform

FWHM:

Full width at half maximum

GPC:

Glycerophosphocholine

HLSVD:

Hankel–Lanczos singular value decomposition

Lac:

Lactate

m-Ino:

Myoinositol

MR:

Magnetic resonance

MRI:

Magnetic resonance imaging

MRS:

Magnetic resonance spectroscopy

MRSI:

Magnetic resonance spectroscopic imaging

NMR:

Nuclear magnetic resonance

PA:

Padé approximant

ppm:

Parts per million

PC:

Phosphocholine

PE:

Phosphoethanolamine

SNR:

Signal-to-noise ratio

SNS:

Signal–noise separation

Tau:

Taurine

TE:

Echo time

TSP:

3-(trimethylsilyl-) 3,3,2,2-tetradeutero-propionic acid

References

  1. Belkić Dž., Belkić K.: Decisive role of mathematical methods in early cancer diagnostics. J. Math. Chem. 42, 1 (2007)

    Article  CAS  Google Scholar 

  2. S. Hankinson, D. Hunter, in Breast Cancer, ed. by H.-O. Adami, D. Hunter, D. Trichopoulos. Textbook of Cancer Epidemiology (Oxford University Press, Oxford, 2002), pp. 301–339

  3. Masood S.: Coming together to conquer the fight against breast cancer in countries of limited resources: the challenges and the opportunities. Breast J. 13, 223 (2007)

    Article  Google Scholar 

  4. Parkin D.M., Fernandez L.M.: Use of statistics to assess the global burden of breast cancer. Breast J. 12(Suppl 1), S70 (2006)

    Article  Google Scholar 

  5. Belkić Dž., Dando P.A., Main J., Taylor H.S.: Three novel high-resolution nonlinear methods for fast signal processing. J. Chem. Phys. 113, 6542 (2000)

    Article  Google Scholar 

  6. Belkić Dž.: Fast Padé Transform (FPT) for magnetic resonance imaging and computerized tomography. Nucl. Instrum. Methods Phys. Res. A 471, 165 (2001)

    Article  Google Scholar 

  7. Belkić Dž.: Strikingly stable convergence of the fast Padé transform (FPT) for high-resolution parametric and non-parametric signal processing of Lorentzian and non-Lorentzian spectra. Nucl. Instrum. Methods. Phys. Res. A 525, 366 (2004)

    Article  CAS  Google Scholar 

  8. Belkić Dž.: Error analysis through residual frequency spectra in the fast Padé transform (FPT). Nucl. Instrum. Methods Phys. Res. A 525, 379 (2004)

    Article  CAS  Google Scholar 

  9. Belkić Dž.: Analytical continuation by numerical means in spectral analysis using the fast Padé transform (FPT). Nucl. Instrum. Methods Phys. Res. A 525, 372 (2004)

    Article  CAS  Google Scholar 

  10. Belkić Dž.: Quantum Mechanical Signal Processing and Spectral Analysis. Institute of Physics Publishing, Bristol (2005)

    Google Scholar 

  11. Belkić Dž., Belkić K.: The fast Padé transform in magnetic resonance spectroscopy for potential improvements in early cancer diagnostics. Phys. Med. Biol. 50, 4385 (2005)

    Article  CAS  Google Scholar 

  12. Belkić Dž.: Exact quantification of time signals in Padé-based magnetic resonance spectroscopy. Phys. Med. Biol. 51, 2633 (2006)

    Article  CAS  Google Scholar 

  13. Belkić Dž.: Exponential convergence rate (the spectral convergence) of the fast Padé transform for exact quantification in magnetic resonance spectroscopy. Phys. Med. Biol. 51, 6483 (2006)

    Article  Google Scholar 

  14. Dž. Belkić, K. Belkić, The general concept of signal-noise separation (SNS): mathematical aspects and implementation in magnetic resonance spectroscopy. J. Math. Chem. (2008) doi:10.1007/s10910-007-9344-5

  15. Dž. Belkić, K. Belkić, Unequivocal disentangling genuine from spurious information in time signals: clinical relevance in cancer diagnostics through magnetic resonance spectroscopy. J. Math. Chem. (2008) doi:10.1007/s10910-007-9337-4.

  16. Belkić Dž., Belkić K.: In vivo magnetic resonance spectroscopy by the fast Padé transform. Phys. Med. Biol. 51, 1049 (2006)

    Article  Google Scholar 

  17. Belkić Dž.: Machine accurate quantification in magnetic resonance spectroscopy. Nucl. Instrum. Methods Phys. Res. A 580, 1034 (2007)

    Google Scholar 

  18. Belkić Dž.: Strikingly stable convergence of the fast Padé transform. J. Comp. Methods Sci. Eng. 3, 299 (2003)

    Google Scholar 

  19. Belkić Dž.: Padé-based magnetic resonance spectroscopy (MRS). J. Comp. Methods Sci. Eng. 3, 563 (2003)

    Google Scholar 

  20. Pijnappel W.W.F., van den Boogaart A., de Beer R., van Ormondt D.: SVD-based quantification of magnetic resonance signals. J. Magn. Reson. 97, 122 (1992)

    Google Scholar 

  21. M. Froissart, Approximation de Padé: application à la Physique des Particules Élémentaires, CNRS, RCP, Programme No. 25, vol. 9 (CNRS, Strasburg, 1969), p. 1

  22. Belkić K.: Resolution performance of the fast Padé transform: potential advantages for magnetic resonance spectroscopy in ovarian cancer diagnostics. Nucl. Instrum. Methods Phys. Res. A 580, 874 (2007)

    Google Scholar 

  23. Belkić Dž., Belkić K.: Mathematical modeling of an NMR chemistry problem in ovarian cancer diagnostics. J. Math. Chem. 43, 395 (2008)

    Article  CAS  Google Scholar 

  24. Saslow D., Boetes C., Burke W., Harms S., M.O. Leach, C.D. Lehman et al.: American Cancer Society Breast Cancer Advisory Group. American Cancer Society guidelines for breast screening with MRI as an adjunct to mammography. CA Cancer J. Clin. 57, 75–89 (2007)

    Article  Google Scholar 

  25. Parkin D.M., Bray F., Pisani P.: Global cancer statistics. CA Cancer J. Clin. 55, 74–108 (2005)

    Article  Google Scholar 

  26. Armstrong K., Moye E., Williams S., Berlin J.A., Reynolds E.E.: Screening mammography in women 40 to 49 years of age: a systematic review for the American College of Physicians. Ann. Intern. Med. 146, 516–526 (2007)

    Google Scholar 

  27. Berg W.A., Blume J.D., Cormack J.B., Mendelson E.B., Lehrer D., Bohm-Velez Pisan M. et al.: ACRIN 6666 Investigators. Combined screening with ultrasound and mammography vs mammography alone in women at elevated risk of breast cancer. JAMA 299, 2151–2163 (2008)

    Article  CAS  Google Scholar 

  28. Perry N., Broeders M., de Wolf C., Törnberg S., Holland R., von Karsa L.: European guidelines for quality assurance in breast cancer screening and diagnosis. Fourth edition—summary document. Ann. Oncol. 19, 614–622 (2008)

    CAS  Google Scholar 

  29. Kuni H., Schmitz-Feuerhake I., Dieckmann H.: Mammography screening—neglected aspects of radiation risks. Gesundheitswesen 65, 44 (2003)

    Google Scholar 

  30. Laderoute M.P.: Improved safety and effectiveness of imaging predicted for MR mammography. Br. J. Cancer 90, 278 (2004)

    Article  CAS  Google Scholar 

  31. Schrading S., Kuhl C.K.: Mammographic, US, and MR imaging phenotypes of familial breast cancer. Radiology 246, 58 (2008)

    Google Scholar 

  32. Kriege M., Brekelmans C.T., Peterse H., Obdeijn I.M., Boetes C., Zonderland H.M. et al.: Tumor characteristics and detection method in the MRISC screening program for the early detection of hereditary breast cancer. Breast Cancer Res. Treat. 102, 357–363 (2007)

    Article  CAS  Google Scholar 

  33. Houssami N., Wilson R.: Should women at high risk of breast cancer have screening magnetic resonance imaging (MRI)?. J. Breast 16, 2 (2007)

    Article  Google Scholar 

  34. S.J. Nass, C. Henderson, J.C. Lashof, (eds.) Mammography and Beyond: Developing Technologies for the Early Detection of Breast Cancers (National Academy Press, Washington, DC, 2001)

  35. Morris E.A.: Breast cancer imaging with MRI. Radiol. Clin. N. Am. 40, 443 (2002)

    Article  Google Scholar 

  36. Yu J., Park A., Morris E., Liberman L., Borgen P.I., King T.A.: MRI screening in a clinic population with a family history of breast cancer. Ann. Surg. Oncol. 15, 452 (2008)

    Article  Google Scholar 

  37. Iglesias A., Arias M., Santiago P., Rodríguez M., Mañas J., Saborido C.: Benign breast lesions that simulate malignancy: magnetic resonance imaging with radiologic-pathologic correlation. Curr. Probl. Diagn. Radiol. 36, 66 (2007)

    Article  Google Scholar 

  38. Essink-Bot M.L., Rijnsburger A.J., van Dooren S., de Koning H.J., Seynaeve C.: Women’s acceptance of MRI in breast cancer surveillance because of a familial or genetic predisposition. Breast 15, 673 (2006)

    Article  CAS  Google Scholar 

  39. Robson M.: Breast cancer surveillance in women with hereditary risk due to BRCA1 or BRCA2 mutations. Clin. Breast Cancer 5, 260 (2004)

    Article  Google Scholar 

  40. Belkić K.: Current dilemmas and future perspectives for breast cancer screening with a focus on optimization of magnetic resonance spectroscopic imaging by advances in signal processing. Isr. Med. Assoc. J. 6, 610–618 (2004)

    Google Scholar 

  41. Katz-Brull R., Lavin P.T., Lenkinski R.E.: Clinical utility of proton magnetic resonance spectroscopy in characterizing breast lesions. J. Natl. Cancer Inst. 9, 1197 (2002)

    Google Scholar 

  42. Bartella L., Morris E.A., Dershaw D.D., Liberman L., Thakur S.B., Moskowitz C., Guido J., Huang W.: Proton MR spectroscopy with choline peak as malignancy marker improves positive predictive value for breast cancer diagnosis: preliminary study. Radiology 239, 686–692 (2006)

    Article  Google Scholar 

  43. Bartella L., Thakur S.B., Morris E.A., Dershaw D.D., Huang W., Chough E., Cruz M.C., Liberman L.: Enhancing nonmass lesions in the breast: evaluation with proton (1H) MR spectroscopy. Radiology 245, 80–87 (2007)

    Article  Google Scholar 

  44. Bartella L., Huang W.: Proton (1H) MR spectroscopy of the breast. Radiographics 27(Suppl 1), S241 (2007)

    Article  Google Scholar 

  45. Meisamy S., Bolan P.J., Baker E.H., Le C.T., Kelcz F., Lechner M.C., Luikens B.A., Carlson R.A., Brandt K.R., Amrami K.K. et al.: Adding in vivo quantitative 1H MR spectroscopy to improve diagnostic accuracy of breast MR imaging: preliminary results of observer performance study at 4.0T. Radiology 236, 465 (2005)

    Article  Google Scholar 

  46. Sardanelli F., Fausto A., Podo F.: MR spectroscopy of the breast. Radiol. Med. (Torino) 113, 56 (2008)

    Article  CAS  Google Scholar 

  47. Tse G.M., Yeung D.K., King A.D., Cheung H.S., Yang W.T.: In vivo proton magnetic resonance spectroscopy of breast lesions: an update. Breast Cancer Res. Treat. 104, 249 (2007)

    Article  Google Scholar 

  48. Kwock L., Smith J.K., Castillo M., Ewend M.G., Collichio F., D.E. Morris, Bouldin T.W., Cush S.: Clinical role of proton magnetic resonance spectroscopy in oncology: brain, breast and prostate cancer. Lancet Oncol. 7, 859 (2006)

    Article  Google Scholar 

  49. Hu J., Yu Y., Kou Z., Huang W., Jiang Q., Xuan Y., Li T., Sehgal V., Blake C., Haacke E.M., Soulen R.L.: A high spatial resolution 1H magnetic resonance spectroscopic imaging technique for breast cancer with a short echo time. Magn. Reson. Imaging 26, 360 (2008)

    Article  Google Scholar 

  50. Stanwell P., Mountford C.: In vivo proton MR spectroscopy of the breast. Radiographics 27(Suppl 1), S253 (2007)

    Article  Google Scholar 

  51. Tozaki M.: Proton MR spectroscopy of the breast. Breast Cancer 15, 218 (2008)

    Article  Google Scholar 

  52. Gluch L.: Magnetic resonance in surgical oncology. ANZ. J. Surg. 75, 464 (2005)

    Article  Google Scholar 

  53. Belkić K.: MR spectroscopic imaging in breast cancer detection: possibilities beyond the conventional theoretical framework for data analysis. Nucl. Instrum. Methods Phys. Res. A. 525, 313 (2004)

    Article  CAS  Google Scholar 

  54. Belkić Dž., Belkić K.: Mathematical optimization of in vivo NMR chemistry through the fast Padé transform: potential relevance for early breast cancer detection by magnetic resonance spectroscopy. J. Math. Chem. 40, 85 (2006)

    Article  CAS  Google Scholar 

  55. Gribbestad I.S., Sitter B., Lundgren S., Krane J., Axelson D: Metabolite composition in breast tumors examined by proton nuclear magnetic resonance spectroscopy. Anticancer Res. 19, 1737 (1999)

    CAS  Google Scholar 

  56. Jacobs M.A., Barker P.B., Bottomley P.A., Bhujwalla Z., Bluemke D.A.: Proton magnetic resonance spectroscopic imaging of human breast cancer: a preliminary study. J. Magn. Reson. Imaging 19, 68 (2004)

    Article  Google Scholar 

  57. Evelhoch J., Garwood M., Vigneron D., Knopp M., Sullivan D., Menkens A. et al.: Expanding the use of magnetic resonance in the assessment of tumor response to therapy. Cancer Res. 65, 7041 (2005)

    Article  CAS  Google Scholar 

  58. Frahm J., Bruhn H., Gyngell M.L., Merboldt K.D., Hanicke W., Sauter R.: Localized high-resolution proton NMR spectroscopy using stimulated echoes: initial applications to human brain in vivo. Magn. Reson. Med. 9, 79 (1989)

    Article  CAS  Google Scholar 

  59. Swanson M.G., Zektzer A.S., Simko J., Simko J., Jarso S., Schmitt K.R.L., Schmitt K.R.L., Carroll P.R., Shinohara K., Vigneron D.B., Kurhanewicz J.: Quantitative analysis of prostate metabolites using 1H HR-MAS spectroscopy. Magn. Reson. Med. 55, 1257 (2006)

    Article  CAS  Google Scholar 

  60. van der Veen J.W., de Beer R., Luyten P.R., van Ormondt D.: Accurate quantification of in vivo 31P NMR signals using the variable projection method and prior knowledge. Magn. Reson. Med. 6, 92 (1988)

    Article  Google Scholar 

  61. Vanhamme L., van den Boogaart A., van Haffel S.: Improved method for accurate and efficient quantification of MRS data with use of prior knowledge. J. Magn. Reson. 29, 35 (1997)

    Article  Google Scholar 

  62. Provencher S.W.: Estimation of metabolite concentrations from localized in vivo proton NMR spectra. Magn. Reson. Med. 30, 672 (1993)

    Article  CAS  Google Scholar 

  63. Aboagye E.O., Bhujwalla Z.M.: Malignant transformation alters membrane choline phospholipid metabolism of human mammary epithelial cells. Cancer Res. 59, 80 (1999)

    CAS  Google Scholar 

  64. Katz-Brull R., Seger D., Rivenson-Segal D., Rushkin E., Degani H.: Metabolic markers of breast cancer: enhanced choline metabolism and reduced choline-ether-phospholipid synthesis. Cancer Res. 62, 1966 (2002)

    CAS  Google Scholar 

  65. Glunde K., Jie C., Bhujwalla Z.M.: Molecular causes of the aberrant choline phospholipid metabolism in breast cancer. Cancer Res. 64, 4270 (2004)

    Article  CAS  Google Scholar 

  66. Sharma U., Mehta A., Seenu V., Jagannathan N.R.: Biochemical characterization of metastatic lymph nodes of breast cancer patients by in vitro 1H magnetic resonance spectroscopy: a pilot study. Magn. Reson. Imaging 22, 697 (2004)

    Article  CAS  Google Scholar 

  67. Rivenzon-Segal D., Margalit R., Degani H.: Glycolysis as a metabolic marker in orthotopic breast cancer, monitored by in vivo 13C MRS. Am. J. Physiol. Endocrinol. Metab. 283, E623 (2002)

    CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dževad Belkić.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Belkić, D., Belkić, K. Exact quantification of time signals from magnetic resonance spectroscopy by the fast Padé transform with applications to breast cancer diagnostics. J Math Chem 45, 790–818 (2009). https://doi.org/10.1007/s10910-008-9462-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10910-008-9462-8

Keywords

Navigation