Bolus arrival time estimation in dynamic contrast-enhanced magnetic resonance imaging of small animals based on spline models
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is used to quantify perfusion and vascular permeability. In most cases a bolus arrival time (BAT) delay exists between the arterial input function (AIF) and the contrast agent arrival in the tissue of interest which needs to be estimated...
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| Hauptverfasser: | , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
5 February 2019
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| In: |
Physics in medicine and biology
Year: 2019, Jahrgang: 64, Heft: 4 |
| ISSN: | 1361-6560 |
| DOI: | 10.1088/1361-6560/aafce7 |
| Online-Zugang: | Verlag, Volltext: https://doi.org/10.1088/1361-6560/aafce7 Verlag: https://iopscience.iop.org/article/10.1088/1361-6560/aafce7 
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| Verfasserangaben: | Alina L. Bendinger, Charlotte Debus, Christin Glowa, Christian P. Karger, Jörg Peter and Martin Storath |
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| 245 | 1 | 0 | |a Bolus arrival time estimation in dynamic contrast-enhanced magnetic resonance imaging of small animals based on spline models |c Alina L. Bendinger, Charlotte Debus, Christin Glowa, Christian P. Karger, Jörg Peter and Martin Storath |
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| 520 | |a Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is used to quantify perfusion and vascular permeability. In most cases a bolus arrival time (BAT) delay exists between the arterial input function (AIF) and the contrast agent arrival in the tissue of interest which needs to be estimated. Existing methods for BAT estimation are tailored to tissue concentration curves, which have a fast upslope to the peak as frequently observed in patient data. However, they may give poor results for curves that do not have this characteristic shape such as tissue concentration curves of small animals. In this paper, we propose a method for BAT estimation of signals that do not have a fast upslope to their peak. The model is based on splines which are able to adapt to a large variety of concentration curves. Furthermore, the method estimates BATs on a continuous time scale. All relevant model parameters are automatically determined by generalized cross validation. We use simulated concentration curves of small animal and patient settings to assess the accuracy and robustness of our approach. The proposed method outperforms a state-of-the-art method for small animal data and it gives competitive results for patient data. Finally, it is tested on in vivo acquired rat data where accuracy of BAT estimation was also improved upon the state-of-the-art method. The results indicate that the proposed method is suitable for accurate BAT estimation of DCE-MRI data, especially for small animals. | ||
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