Model-Based Parameter estimation in DCE-MRI Without an Arterial Input Function

Constantin Heck; Lars Ruthotto; Jan Modersitzki; Benjamin Berkels

doi:10.1007/978-3-642-54111-7_47

Model-Based Parameter estimation in DCE-MRI Without an Arterial Input Function

Constantin Heck, Lars Ruthotto, Jan Modersitzki, Benjamin Berkels

Institute of Mathematics and Image Computing

Abstract

Analysis of DCE-MRI data is often carried out by fitting parametric models. However, one major factor of uncertainty is the determination of the arterial input function (AIF). We introduce a novel approach to estimate kinetic parameters in DCE-MRI without an AIF. An existing method by Riabkov et al., where the AIF is introduced as an additional unknown, is extended by the addition of spatial diffusive regularization of the parameter maps and a control term for the scale of the AIF. We validate our method on artificial data, where it significantly reduces the relative error as compared to the original method by Riabkov. Additionally, we present first promising results on real data.

Original language	English
Title of host publication	Bildverarbeitung für die Medizin 2014
Editors	Thomas Martin Deserno, Heinz Handels, Hans-Peter Meinzer, Thomas Tolxdorff
Number of pages	6
Place of Publication	Berlin, Heidelberg
Publisher	Springer Berlin Heidelberg
Publication date	04.03.2014
Pages	246-251
ISBN (Print)	978-3-642-54110-0
ISBN (Electronic)	978-3-642-54111-7
DOIs	https://doi.org/10.1007/978-3-642-54111-7_47
Publication status	Published - 04.03.2014
Event	Workshop on Bildverarbeitung fur die Medizin 2014 - Aachen, Germany Duration: 16.03.2014 → 18.03.2014

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Cite this

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title = "Model-Based Parameter estimation in DCE-MRI Without an Arterial Input Function",

abstract = "Analysis of DCE-MRI data is often carried out by fitting parametric models. However, one major factor of uncertainty is the determination of the arterial input function (AIF). We introduce a novel approach to estimate kinetic parameters in DCE-MRI without an AIF. An existing method by Riabkov et al., where the AIF is introduced as an additional unknown, is extended by the addition of spatial diffusive regularization of the parameter maps and a control term for the scale of the AIF. We validate our method on artificial data, where it significantly reduces the relative error as compared to the original method by Riabkov. Additionally, we present first promising results on real data.",

author = "Constantin Heck and Lars Ruthotto and Jan Modersitzki and Benjamin Berkels",

year = "2014",

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day = "4",

doi = "10.1007/978-3-642-54111-7_47",

language = "English",

isbn = "978-3-642-54110-0",

series = "Informatik aktuell",

publisher = "Springer Berlin Heidelberg",

pages = "246--251",

editor = "Deserno, {Thomas Martin} and Heinz Handels and Hans-Peter Meinzer and Thomas Tolxdorff",

booktitle = "Bildverarbeitung f{\"u}r die Medizin 2014",

note = "Workshop on Bildverarbeitung fur die Medizin 2014 ; Conference date: 16-03-2014 Through 18-03-2014",

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Heck, C, Ruthotto, L, Modersitzki, J & Berkels, B 2014, Model-Based Parameter estimation in DCE-MRI Without an Arterial Input Function. in TM Deserno, H Handels, H-P Meinzer & T Tolxdorff (eds), Bildverarbeitung für die Medizin 2014. Informatik aktuell, Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 246-251, Workshop on Bildverarbeitung fur die Medizin 2014, Aachen, Germany, 16.03.14. https://doi.org/10.1007/978-3-642-54111-7_47

Model-Based Parameter estimation in DCE-MRI Without an Arterial Input Function. / Heck, Constantin; Ruthotto, Lars; Modersitzki, Jan et al.
Bildverarbeitung für die Medizin 2014. ed. / Thomas Martin Deserno; Heinz Handels; Hans-Peter Meinzer; Thomas Tolxdorff. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. p. 246-251 (Informatik aktuell).

Research output: Chapters in Books/Reports/Conference Proceedings › Chapter › peer-review

TY - CHAP

T1 - Model-Based Parameter estimation in DCE-MRI Without an Arterial Input Function

AU - Heck, Constantin

AU - Ruthotto, Lars

AU - Modersitzki, Jan

AU - Berkels, Benjamin

PY - 2014/3/4

Y1 - 2014/3/4

N2 - Analysis of DCE-MRI data is often carried out by fitting parametric models. However, one major factor of uncertainty is the determination of the arterial input function (AIF). We introduce a novel approach to estimate kinetic parameters in DCE-MRI without an AIF. An existing method by Riabkov et al., where the AIF is introduced as an additional unknown, is extended by the addition of spatial diffusive regularization of the parameter maps and a control term for the scale of the AIF. We validate our method on artificial data, where it significantly reduces the relative error as compared to the original method by Riabkov. Additionally, we present first promising results on real data.

AB - Analysis of DCE-MRI data is often carried out by fitting parametric models. However, one major factor of uncertainty is the determination of the arterial input function (AIF). We introduce a novel approach to estimate kinetic parameters in DCE-MRI without an AIF. An existing method by Riabkov et al., where the AIF is introduced as an additional unknown, is extended by the addition of spatial diffusive regularization of the parameter maps and a control term for the scale of the AIF. We validate our method on artificial data, where it significantly reduces the relative error as compared to the original method by Riabkov. Additionally, we present first promising results on real data.

U2 - 10.1007/978-3-642-54111-7_47

DO - 10.1007/978-3-642-54111-7_47

M3 - Chapter

SN - 978-3-642-54110-0

T3 - Informatik aktuell

SP - 246

EP - 251

BT - Bildverarbeitung für die Medizin 2014

A2 - Deserno, Thomas Martin

A2 - Handels, Heinz

A2 - Meinzer, Hans-Peter

A2 - Tolxdorff, Thomas

PB - Springer Berlin Heidelberg

CY - Berlin, Heidelberg

T2 - Workshop on Bildverarbeitung fur die Medizin 2014

Y2 - 16 March 2014 through 18 March 2014

ER -

Model-Based Parameter estimation in DCE-MRI Without an Arterial Input Function

Abstract

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