Publication detail

Distributed capillary adiabatic tissue homogeneity model in parametric multi-channel blind AIF estimation using DCE-MRI

KRATOCHVÍLA, J. JIŘÍK, R. BARTOŠ, M. STANDARA, M. STARČUK, Z. TAXT, T.

Original Title

Distributed capillary adiabatic tissue homogeneity model in parametric multi-channel blind AIF estimation using DCE-MRI

Czech Title

Distributed capillary adiabatic tissue homogeneity model in parametric multi-channel blind AIF estimation using DCE-MRI

English Title

Distributed capillary adiabatic tissue homogeneity model in parametric multi-channel blind AIF estimation using DCE-MRI

Type

journal article

Language

en

Original Abstract

Purpose One of the main challenges in quantitative dynamic contrast-enhanced (DCE) MRI is estimation of the arterial input function (AIF). Usually, the signal from a single artery (ignoring contrast dispersion, partial volume effects and flow artifacts) or a population average of such signals (also ignoring variability between patients) is used. Methods Multi-channel blind deconvolution is an alternative approach avoiding most of these problems. The AIF is estimated directly from the measured tracer concentration curves in several tissues. This contribution extends the published methods of multi-channel blind deconvolution by applying a more realistic model of the impulse residue function, the distributed capillary adiabatic tissue homogeneity model (DCATH). In addition, an alternative AIF model is used and several AIF-scaling methods are tested. Results The proposed method is evaluated on synthetic data with respect to the number of tissue regions and to the signal-to-noise ratio. Evaluation on clinical data (renal cell carcinoma patients before and after the beginning of the treatment) gave consistent results. An initial evaluation on clinical data indicates more reliable and less noise sensitive perfusion parameter estimates. Conclusion Blind multi-channel deconvolution using the DCATH model might be a method of choice for AIF estimation in a clinical setup. Magn Reson Med, 2015. © 2015 Wiley Periodicals, Inc.

Czech abstract

Purpose One of the main challenges in quantitative dynamic contrast-enhanced (DCE) MRI is estimation of the arterial input function (AIF). Usually, the signal from a single artery (ignoring contrast dispersion, partial volume effects and flow artifacts) or a population average of such signals (also ignoring variability between patients) is used. Methods Multi-channel blind deconvolution is an alternative approach avoiding most of these problems. The AIF is estimated directly from the measured tracer concentration curves in several tissues. This contribution extends the published methods of multi-channel blind deconvolution by applying a more realistic model of the impulse residue function, the distributed capillary adiabatic tissue homogeneity model (DCATH). In addition, an alternative AIF model is used and several AIF-scaling methods are tested. Results The proposed method is evaluated on synthetic data with respect to the number of tissue regions and to the signal-to-noise ratio. Evaluation on clinical data (renal cell carcinoma patients before and after the beginning of the treatment) gave consistent results. An initial evaluation on clinical data indicates more reliable and less noise sensitive perfusion parameter estimates. Conclusion Blind multi-channel deconvolution using the DCATH model might be a method of choice for AIF estimation in a clinical setup. Magn Reson Med, 2015. © 2015 Wiley Periodicals, Inc.

English abstract

Purpose One of the main challenges in quantitative dynamic contrast-enhanced (DCE) MRI is estimation of the arterial input function (AIF). Usually, the signal from a single artery (ignoring contrast dispersion, partial volume effects and flow artifacts) or a population average of such signals (also ignoring variability between patients) is used. Methods Multi-channel blind deconvolution is an alternative approach avoiding most of these problems. The AIF is estimated directly from the measured tracer concentration curves in several tissues. This contribution extends the published methods of multi-channel blind deconvolution by applying a more realistic model of the impulse residue function, the distributed capillary adiabatic tissue homogeneity model (DCATH). In addition, an alternative AIF model is used and several AIF-scaling methods are tested. Results The proposed method is evaluated on synthetic data with respect to the number of tissue regions and to the signal-to-noise ratio. Evaluation on clinical data (renal cell carcinoma patients before and after the beginning of the treatment) gave consistent results. An initial evaluation on clinical data indicates more reliable and less noise sensitive perfusion parameter estimates. Conclusion Blind multi-channel deconvolution using the DCATH model might be a method of choice for AIF estimation in a clinical setup. Magn Reson Med, 2015. © 2015 Wiley Periodicals, Inc.

Keywords

dynamic contrast-enhanced magnetic resonance imaging;multi-channel blind deconvolution;arterial input function;impulse residue function;renal cell carcinoma

RIV year

2015

Released

13.04.2015

Publisher

John Wiley & Sons, Inc.

Pages from

1

Pages to

11

Pages count

11

URL

BibTex


@article{BUT115759,
  author="Jiří {Kratochvíla} and Radovan {Jiřík} and Michal {Bartoš} and Michal {Standara} and Zenon {Starčuk} and Torfinn {Taxt}",
  title="Distributed capillary adiabatic tissue homogeneity model in parametric multi-channel blind AIF estimation using DCE-MRI",
  annote="Purpose
One of the main challenges in quantitative dynamic contrast-enhanced (DCE) MRI is estimation of the arterial input function (AIF). Usually, the signal from a single artery (ignoring contrast dispersion, partial volume effects and flow artifacts) or a population average of such signals (also ignoring variability between patients) is used.
Methods
Multi-channel blind deconvolution is an alternative approach avoiding most of these problems. The AIF is estimated directly from the measured tracer concentration curves in several tissues. This contribution extends the published methods of multi-channel blind deconvolution by applying a more realistic model of the impulse residue function, the distributed capillary adiabatic tissue homogeneity model (DCATH). In addition, an alternative AIF model is used and several AIF-scaling methods are tested.
Results
The proposed method is evaluated on synthetic data with respect to the number of tissue regions and to the signal-to-noise ratio. Evaluation on clinical data (renal cell carcinoma patients before and after the beginning of the treatment) gave consistent results. An initial evaluation on clinical data indicates more reliable and less noise sensitive perfusion parameter estimates.
Conclusion
Blind multi-channel deconvolution using the DCATH model might be a method of choice for AIF estimation in a clinical setup. Magn Reson Med, 2015. © 2015 Wiley Periodicals, Inc.",
  address="John Wiley & Sons, Inc.",
  chapter="115759",
  doi="10.1002/mrm.25619",
  howpublished="online",
  institution="John Wiley & Sons, Inc.",
  number="-",
  volume="-",
  year="2015",
  month="april",
  pages="1--11",
  publisher="John Wiley & Sons, Inc.",
  type="journal article"
}