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How the fast Padé transform handles noise for MRS data from the ovary: importance for ovarian cancer diagnostics

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Abstract

Advanced signal processing through the fast Padé transform (FPT) can enhance resolution and generates quantitative metabolic information for magnetic resonance spectroscopy (MRS). Herein, we apply both \(\hbox {FPT}^{(\pm )}\) variants to in vitro MRS data as encoded from benign and malignant ovarian cyst fluid and perform detailed analysis with several noise levels. In the presence of higher background noise, all genuine metabolites were unambiguously identified and their concentrations precisely computed, using small fractions of the total signal length by both FPT variants. In the \(\hbox {FPT}^{(-)}\), signal–noise separation was accomplished with the help of the “Stability test”, whereby the non-physical information is binned and denoised spectra are generated. In the \(\hbox {FPT}^{(+)}\) even more stringent signal–noise separation was achieved: the spurious resonances reside exclusively in the negative imaginary frequency domain, whereas the genuine content is all in the positive imaginary frequency region. Pole-zero coincidence of spurious resonance remained complete in the \(\hbox {FPT}^{(+)}\) even at higher noise levels. Via the \(\hbox {FPT}^{(+)}\), a denoised spectrum is generated automatically, without the need for the “Stability test”. The two variants \(\hbox {FPT}^{(\pm )}\) provide self-contained cross-validation of the reconstructed spectral parameters, from which the metabolite concentrations of benign and malignant ovarian cyst fluid are reliably computed. These results are particularly promising for more effective ovarian cancer diagnostics, overcoming the major obstacles that have hindered MRS from becoming the method of choice for non-invasive assessment of ovarian lesions.

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Notes

  1. Serum cancer antigen, CA-125 is a protein whose presence is often associated with ovarian cancer. However, it has poor sensitivity for early stage malignancy and is also non-specific, being present in other cancers, as well as in several non-malignant conditions, including pregnancy.

  2. In Ref. [40] the total number of benign gynecological lesions is given, and the various types described. From this description it can be deduced that at least two and at most six of these lesions were ovarian.

Abbreviations

Ala:

Alanine

au:

Arbitrary units

BW:

Bandwidth

Cho:

Choline

Crn:

Creatinine

COSY:

2D correlated spectroscopy

Cr:

Creatine

DWI:

Diffusion weighted imaging

FFT:

Fast Fourier transform

FID:

Free induction decay

FPT:

Fast Padé transform

Iso:

Isoleucine

Glc:

Glucose

Gln:

Glutamine

Lac:

Lactate

Lys:

Lysine

MRI:

Magnetic resonance imaging

MRS:

Magnetic resonance spectroscopy

Met:

Methionine

PLCO:

Prostate, lung, colon, and ovarian (trial)

ppm:

Parts per million

RMS:

Root-mean-square

SCS:

Statistical classification strategy

SNR:

Signal to noise ratio

SNS:

Signal–noise separation

Thr:

Threonine

TR:

Repetition time

TVUS:

Transvaginal ultrasound

Val:

Valine

ww:

Wet weight

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Acknowledgments

This work was supported by King Gustav the 5th Jubilee Fund, Cancerfonden, the Karolinska Institute Research Fund and FoUU through Stockholm County Council to which the authors are grateful.

Conflict of interest

The authors declare that they have no conflict of interest.

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Authors and Affiliations

  1. Department of Oncology-Pathology, Karolinska Institute, Building P-9, 2nd Floor, P.O. Box 260, 17176, Stockholm, Sweden

    Dževad Belkić & Karen Belkić

  2. School of Community and Global Health, Claremont Graduate University, Claremont, CA, USA

    Karen Belkić

  3. Institute for Prevention Research, Keck School of Medicine, University of Southern California, Alhambra, CA, USA

    Karen Belkić

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  1. Dževad Belkić
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Belkić, D., Belkić, K. How the fast Padé transform handles noise for MRS data from the ovary: importance for ovarian cancer diagnostics. J Math Chem 54, 149–185 (2016). https://doi.org/10.1007/s10910-015-0555-x

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