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The general concept of signal–noise separation (SNS): mathematical aspects and implementation in magnetic resonance spectroscopy

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Abstract

Magnetic resonance spectroscopy (MRS) and spectroscopic imaging (MRSI) are increasingly recognized as potentially key modalities in cancer diagnostics. It is, therefore, urgent to overcome the shortcomings of current applications of MRS and MRSI. We explain and substantiate why more advanced signal processing methods are needed, and demonstrate that the fast Padé transform (FPT), as the quotient of two polynomials, is the signal processing method of choice to achieve this goal. In this paper, the focus is upon distinguishing genuine from spurious (noisy and noise-like) resonances; this has been one of the thorniest challenges to MRS. The number of spurious resonances is always several times larger than the true ones. Within the FPT convergence is achieved through stabilization or constancy of the reconstructed frequencies and amplitudes. This stabilization is a veritable signature of the exact number of resonances. With any further increase of the partial signal length N, towards the full signal length N, i.e., passing the stage at which full convergence has been reached, it is found that all the fundamental frequencies and amplitudes “stay put”, i.e., they still remain constant. Moreover, machine accuracy is achieved here, proving that when the FPT is nearing convergence, it approaches straight towards the exact result with an exponential convergence rate (the spectral convergence). This proves that the FPT is an exponentially accurate representation of functions customarily encountered in spectral analysis in MRS and beyond. The mechanism by which this is achieved, i.e., the mechanism which secures the maintenance of stability of all the spectral parameters and, by implication, constancy of the estimate for the true number of resonances is provided by the so-called pole-zero cancellation, or equivalently, the Froissart doublets. This signifies that all the additional poles and zeros of the Padé spectrum will cancel each other, a remarkable feature unique to the FPT. The FPT is safe-guarded against contamination of the final results by extraneous resonances, since each pole due to spurious resonances stemming from the denominator polynomial will automatically coincide with the corresponding zero of the numerator polynomial, thus leading to the pole-zero cancellation in the polynomial quotient of the FPT. Such pole-zero cancellations can be advantageously exploited to differentiate between spurious and genuine content of the signal. Since these unphysical poles and zeros always appear as pairs in the FPT, they are viewed as doublets. Therefore, the pole-zero cancellation can be used to disentangle noise as an unphysical burden from the physical content in the considered signal, and this is the most important usage of the Froissart doublets in MRS. The general concept of signal–noise separation (SNS) is thereby introduced as a reliable procedure for separating physical from non-physical information in MRS, MRSI and beyond.

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Abbreviations

Ala:

Alanine

AMARES:

Advanced Method for Accurate Robust and Efficient Spectral fitting

Asp:

Aspartate

au:

Arbitrary units

BPH:

Benign prostatic hypertrophy

Cho:

Choline

Cr:

Creatine

Crn:

Creatinine

CT:

Computerized tomography

FID:

Free induction decay

FFT:

Fast Fourier transform

FPT:

Fast Padé transform

GABA:

Gamma amino butyric acid

Glu:

Glutamate

Gln:

Glutamine

Glc:

Glucose

GPC:

Glycerophosphocholine

1H MRS:

Proton magnetic resonance spectroscopy

HLSVD:

Hankel–Lanczos Singular Value Decomposition

Ins:

Inositol

Iso:

Isoleucine (Iso)

Lac:

Lactate

LCModel:

Linear Combination of Model in vitro Spectra

Lip:

Lipid

Lys:

Lysine

Met:

Methionine

MR:

Magnetic resonance

MRI:

Magnetic resonance imaging

MRS:

Magnetic resonance spectroscopy

MRSI:

Magnetic resonance spectroscopic imaging

ms:

Milliseconds

NAA:

N-acetyl aspartate

NAAG:

N-acetyl aspartyl glutamate

NMR:

Nuclear magnetic resonance

PA:

Padé approximant

PCho:

Phosphocholine

PCr:

Phoshocreatine

PET:

Positron emission tomography

ppm:

Parts per million

PSA:

Prostate specific antigen

RT:

Radiation therapy

SNR:

Signal-to-noise ratio

SNS:

Signal–noise separation

Tau:

Taurine

TE:

Echo time

Thr:

Threonine

Val:

Valine

VARPRO:

Variable Projection Method

ww:

Wet weight

1D:

One dimensional

2D:

Two dimensional

References

  1. Bezabeh T., Odlum O., Nason R., Kerr P., Sutherland D., Pael R. (2005) I.C.P. Smith, Prediction of treatment response in head and neck cancer by magnetic resonance spectroscopy. Am. J. Neuroradiol. 26: 2108

    Google Scholar 

  2. Bolan P.J., Nelson M.T.,Yee D., Garwood M. (2005) Imaging in breast cancer: magnetic resonance spectroscopy. Breast Cancer Res. 7: 149

    Article  CAS  Google Scholar 

  3. J. Evelhoch, M. Garwood, D. Vigneron, M. Knopp, D. Sullivan, A.Menkens, 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 

  4. Hollingworth W., Medina L.S., Lenkinski R.E., Shibata D.K., Bernal B., Zurakowski D., Comstock B., Jarvik J.G. (2006) A systematic literature review of magnetic resonance spectroscopy for the characterization of brain tumors. Am. J. Neuroradiol. 27: 1404

    CAS  Google Scholar 

  5. Hricak H. (2005) MR imaging and MR spectroscopic imaging in the pre-treatment evaluation of prostate cancer. Br. J. Radiol. 78: S103

    Article  Google Scholar 

  6. Huzjan R., Sala E., Hricak H. (2005) Magnetic resonance imaging and magnetic resonance spectroscopic imaging of prostate cancer. Nat. Clin. Pract. Urol. 2: 434

    Article  CAS  Google Scholar 

  7. King A.D., Yeung D.K.W., Ahuja A.T., Tse G.M.K., Yuen H.Y., Wong K.T., van Hasselt A.C. (2005) Salivary gland tumors at in vivo MR spectroscopy. Radiology 237: 563

    Article  Google Scholar 

  8. King A.D., Yeung D.K.W., Ahuja A.T., Tse G.M.K., Chan A.B.W., Lam S.S.L., van Hasselt A.C.(2005) In vivo 1H MR spectroscopy of thyroid cancer. Eur. J. Radiol. 54: 112

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. Katz S., Rosen M. (2006) MR imaging and MR spectroscopy in prostate cancer management. Radiol. Clin. N. Am. 44: 723

    Article  Google Scholar 

  11. Mankoff D. (2005) Imaging in breast cancer—revisited. Breast Cancer Res. 7: 276

    Article  Google Scholar 

  12. Payne G.S., Leach M.O. (2006) Applications of magnetic resonance spectroscopy in radiotherapy treatment planning. Br. J. Radiol. 79: S16

    Article  CAS  Google Scholar 

  13. Sibtain N.A., Howe F.A., Saunders D.E. (2007) The clinical value of proton magnetic resonance spectroscopy in adult brain tumours. Clin. Radiol. 62: 109

    Article  CAS  Google Scholar 

  14. Belkić K. (2004) Molecular Imaging through Magnetic Resonance for Clinical Oncology. Cambridge International Science Publishing, Cambridge

    Google Scholar 

  15. Belkić K., Dž. Belkić (2004) Spectroscopic imaging through magnetic resonance for brain tumour diagnostics. J. Comp. Method Sci. Eng. 4: 157

    Google Scholar 

  16. Nelson S. (2003) Multivoxel magnetic resonance spectroscopy of brain tumors. Mol. Cancer Ther. 2: 497

    CAS  Google Scholar 

  17. Howe F.A., Opstad K.S. (2003) 1 H spectroscopy of brain tumors and masses. NMR Biomed. 16, 123

    Article  CAS  Google Scholar 

  18. Dhingsa R., Qayyum A., Coakley F.V., Lu Y., Jones K.D., Swanson M.G., Carroll P.R., Hricak H., J. Kurhanewicz (2004) Prostate cancer localization with endorectal MR imaging and MR spectroscopic imaging: effect of clinical data on reader accuracy. Radiology 230: 215

    Article  Google Scholar 

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

    Article  Google Scholar 

  20. Jagannathan N.R., Kumar M., Seenu V., Coshic O., Dwivedi S.N., Julka P.K., Srivastava A., Rath G.K. (2001) Evaluation of total choline from in-vivo volume localized proton MR spectroscopy and its response to neoadjuvant chemotherapy in locally advanced breast cancer. Br. J. Cancer 84: 1016

    Article  CAS  Google Scholar 

  21. Meisamy S., Bolan P.J., Baker E.H., Bliss R.L., Gulbahce E., Everson L.I. Nelson M.T., Emory T.H., Tuttle T.M., Yee D., Garwood M. (2004) Neoadjuvant chemotherapy of locally advanced breast cancer: predicting response with in vivo 1H MR spectroscopy—a pilot study at 4T. Radiology 233, 424

    Article  Google Scholar 

  22. Stretch J.R., Somorjai R., Bourne R., Hsiao E., Scolyer R.A., Dolenko B., Thompson J.F., Mountford C.E., Lean C.L. (2005) Melanoma metastases in regional lymph nodes are accurately detected by proton magnetic resonance spectroscopy of fine-needle aspirate biopsy samples. Ann. Surg. Oncol. 12: 943

    Article  Google Scholar 

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

    Article  Google Scholar 

  24. Belkić K. (2004) Current dilemmas and future perspectives for breast cancer screening with a focus upon optimization of MR spectroscopic imaging by advances in signal processing. Isr. Med. Assoc. J. 6: 610

    Google Scholar 

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

    CAS  Google Scholar 

  26. Katz-Brull R., Lavin P.T., Lenkinski R.E. (2002) Clinical utility of proton MR spectroscopy in characterizing breast lesions. J. Natl. Cancer Inst. 94:1197

    CAS  Google Scholar 

  27. Kaminogo M., Ishimaru H., Morikawa M., Ochi M., Ushijima R., Tani M., Matsuo Y., Kawakubo J., Shibata S. (2001) iagnostic potential of short echo time MR spectroscopy of gliomas with single-voxel and point-resolved spatially localised proton spectroscopy of brain. Neuroradiology 43:353

    Article  CAS  Google Scholar 

  28. Griffiths J., Tate A.R., Howe F.A., Stubbs M. (2002) as part of the Multi-institutional group on MRS application to cancer. Magnetic resonance spectroscopy of cancer—practicalities of multi-centre trials and early results in non-Hodgkin’s lymphoma. Eur. J. Cancer 38: 2085

    Article  CAS  Google Scholar 

  29. Smith I.C., Blandford D.E. (1998), Diagnosis of cancer in humans by 1H NMR of tissue biopsies. Biochem. Cell Biol. 76: 472

    Article  CAS  Google Scholar 

  30. Wallace J.C., Raaphorst G.P., Somorjai R.L., Ng C.E., Fung Kee Fung M., Senterman M., Smith I.C. (1997)Classification of 1H MR spectra of biopsies from untreated and recurrent ovarian cancer using linear discriminant analysis. Magn. Reson. Med. 38: 569

    Article  CAS  Google Scholar 

  31. Boss E., Moolenaar S.H., Massuger L.F.A.G., Boonstra H., Engelke U.F.H., de Jong J.G.N., Wevers R.A. (2000) High-resolution proton nuclear magnetic resonance spectroscopy of ovarian cyst fluid. NMR Biomed. 13: 297

    Article  CAS  Google Scholar 

  32. Massuger L.F.A.G., van Vierzen P.B.J., Engelke U., Heerschap A. (1998) 1H-MR spectroscopy. A new technique to discriminate benign from malignant ovarian tumors. Cancer 82: 1726

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  34. Wirestam R., Ståhlberg F. (2005) Wavelet-based noise reduction for improved deconvolution of time-series data in dynamic susceptibility-contrast MRI. MAGMA 18: 113

    Article  CAS  Google Scholar 

  35. Belkić Dž. (2004) Quantum Mechanical Signal Processing and Spectral Analysis. Institute of Physics Publishing, Bristol

    Google Scholar 

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

    Article  Google Scholar 

  37. 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. doi:10.1007/s10910-007-9337-4

  38. Maudsley A. (2005) Can MR spectroscopy ever be simple and effective?. Am. J. Neuroradiol. 69: 2167

    Google Scholar 

  39. Lanczos C. (1956) Applied Analysis. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  40. Istratov A.A., Virenko O.F. (1999) Exponential analysis in physical phenomena. Rev. Sci. Instrum. 70:1233

    Article  CAS  Google Scholar 

  41. Bottomley P.A. (1992) The trouble with spectroscopy papers. J. Magn. Reson. Imaging 2: 1

    Article  CAS  Google Scholar 

  42. Opstad K.S., Provencher S.W., Bell B.A., Griffiths J.R., Howe F.A. (2003) Detection of elevated glutathione in meningiomas by quantitative in vivo 1H MRS. Magn. Reson. Med. 49: 632

    Article  CAS  Google Scholar 

  43. Cho Y.-D., Choi G.-H., Lee S.-P., Kim J.-K. (2003) 1H-MRS metabolic patterns for distinguishing between meningiomas and other brain tumors. Magn. Reson. Imaging 21: 663

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  45. Danielsen E., Ross B. (1999) Magnetic Resonance Spectroscopy Diagnosis of Neurological Diseases. Marcel Dekker, Inc., New York

    Google Scholar 

  46. Brandão L. (2004) Domingues R. MR Spectroscopy of the Brain. Lippincott Williams & Wilkins, Philadelphia

    Google Scholar 

  47. Belkić Dž.(2004) 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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  53. Callaghan M.F., Larkman D.J., Hajnal J.V. (2005) Padé methods for reconstruction and feature extraction in magnetic resonance imaging. Magn. Reson. Med. 54: 1490

    Article  Google Scholar 

  54. Belkić Dž, Belkić K. (2005) Fast Padé transform for optimal quantification of time signals from magnetic resonance spectroscopy. Int. J. Quant. Chem. 105:493

    Article  Google Scholar 

  55. Tkáč I., Andersen P., Adriany G., Merkle H., Ugurbil K., Gruetter R. (2001) In vivo 1H NMR spectroscopy of the human brain at 7T. Magn. Reson. Med. 46: 451

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  60. Belkić Dž (2006) Fast Padé transform for exact quantification of time signals in magnetic resonance spectroscopy. Adv. Quant. Chem. 51: 157

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  67. Govindaraju V., Young K., Maudsley A.A. (2000) Proton NMR chemical shifts and coupling constants for brain metabolites. NMR Biomed. 13: 129

    Article  CAS  Google Scholar 

  68. Swindle P., McCredie S., Russell P., Himmelreich U., Khadra M., Lean C., Mountford C. (2003) Pathologic characterization of human prostate tissue with proton MR spectroscopy. Radiology 228: 144

    Article  Google Scholar 

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

    Google Scholar 

  70. Froissart M. (1969) Approximation de Padé: Application à la Physique des Particules Élémentaires, CNRS, RCP, Programme No. 25. Strasbourg 9: 1

    Google Scholar 

  71. Jolesz F. (2005) Future of magnetic resonance imaging and magnetic resonance spectroscopy in oncology. ANZ J. Surg. 75:372

    Article  Google Scholar 

  72. Mountford C.E., Doran S., Lean C., Russell P. (2004) Proton MRS can determine the pathology of human cancers with a high level of accuracy. Chem. Rev. 104, 3677

    Article  CAS  Google Scholar 

  73. Belkić Dž., Belkić K. (2006) 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

    Article  Google Scholar 

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

    CAS  Google Scholar 

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

    CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  77. K. Belkić, Padé-optimized magnetic resonance spectroscopy: New possibilities for early breast cancer detection. Medicinteknikdagarna, October 2006, Uppsala

  78. Kriege M., Brekelmans C.T.M., Boetes C., Besnard P.E., Zonderland H.M., Obdeijn I.M. et al. (2004) Efficacy of MRI and mammography for breast-cancer screening in women with a familial or genetic predisposition. N. Engl. J. Med. 351, 427

    Article  CAS  Google Scholar 

  79. Kuhl C.K., Scharding S., Leutner C.C., Markkabati-Spitz N., Wardelmann E., Fimmers R. et al. (2005) Mammography, breast ultrasound, and magnetic resonance imaging for surveillance of women at high familial risk for breast cancer. J. Clin. Oncol. 23, 8469

    Article  Google Scholar 

  80. J.A. Smith, E. Andreopoulou, An overview of the status of imaging screening technology for breast cancer. Ann. Oncol. 15(Suppl. 1), i18 (2004)

  81. Memorial Sloan-Kettering, cited by: T. Freeman, Medical PhysicsWeb.org Newswire Week 47, 2007

  82. Kuranewicz J., Swanson M.G., Nelson S.J., Vigneron D.B. (2002) Combined magnetic resonance imaging and spectroscopic imaging approach to molecular imaging of prostate cancer. J. Magn. Reson. Imaging 16, 451

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  84. Chen A.P., Cunningham C.H., Kurhanewicz J., Xu D., Hurd R.E., Pauly J.M., Carvajal L., Karpodinis K., Vigneron D.B. (2006) High-resolution 3D MR spectroscopic imaging of the prostate at 3T with the MLEV-PRESS sequence. Magn. Reson. Imaging 24, 825

    Article  CAS  Google Scholar 

  85. Lean C.L., Bourne R., Thompson J.F., Scolyer R.A., Stretch J., Li L.X., Russell P., Mountford C. (2003) Rapid detection of metastatic melanoma in lymph nodes using proton magnetic resonance spectroscopy of fine needle aspiration biopsy specimens. Melanoma Res. 13, 259

    Article  Google Scholar 

  86. J.F. Thompson, J.R. Stretch, R.F. Uren, V.S. Ka, R.A. Scolyer, Sentinel node biopsy for melanoma: Where have we been and where are we going? Ann. Surg. Oncol. 11(Suppl.), 147S (2004)

  87. Dž. Belkić, K. Belkić, High-resolution magnetic resonance imaging (MRI), Medical Imaging Conference IEEE (MIC), Portland, October 22–25, 2003 Abstract Number 1971 (CD)

  88. Thomas M.A., Wyckoff N., Yue K., Binesh N., Banakar S., Chung H-K., Sayre J., DeBruhl N. (2005) Two-dimensional MR spectroscopy characterization of breast cancer in vivo. Two-dimensional MR spectroscopy characterization of breast cancer in vivo. Technol. Cancer Res. Treat. 4, 99

    Google Scholar 

  89. Dž. Belkić, High-resolution parametric estimation of two-dimensional magnetic resonance spectroscopy, 20th Annual Meeting of European Soc. Magn. Res. Med. Biol. (ESMRMB), Abstract Number 365 (CD), Rotterdam (Netherlands), September 18–21, 2003

  90. K. Belkić, Dž. Belkić. The fast Padé transform (FPT) for magnetic resonance spectroscopic imaging (MRSI) in oncology, Medical Imaging Conference IEEE (MIC), Portland, October 22–25, 2003 Abstract Number 1918 (CD), Portland (Oregon, USA), October 22–25, 2003

  91. Park I., Tamai G., Lee M.C., Chuang C.F., Chang S.M., Berger M.S., Nelson S.J., Pirzkall A. (2007) Patterns of recurrence analysis in newly diagnosed glioblastoma multiforme after three-dimensional conformal radiation therapy with respect to pre-radiation therapy magnetic resonance spectroscopic findings. Int. J. Radiat. Oncol. Biol. Phys. 69, 381

    Google Scholar 

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Belkić, D., Belkić, K. The general concept of signal–noise separation (SNS): mathematical aspects and implementation in magnetic resonance spectroscopy. J Math Chem 45, 563–597 (2009). https://doi.org/10.1007/s10910-007-9344-5

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