Detail publikace

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.

Originální název

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

Český název

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

Anglický název

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

Typ

článek v časopise

Jazyk

en

Originální abstrakt

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.

Český abstrakt

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.

Anglický abstrakt

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.

Rok RIV

2015

Vydáno

13.04.2015

Nakladatel

John Wiley & Sons, Inc.

Strany od

1

Strany do

11

Strany počet

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"
}