Blind deconvolution estimation of an arterial input function for small animal DCE-MRI

One of the main obstacles for reliable quantitative dynamic contrast-enhanced (DCE) MRI is the need for accurate knowledge of the arterial input function (AIF). This is a special challenge for preclinical small animal applications where it is very difficult to measure the AIF without partial volume...

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Bibliographic Details
Published in:Magnetic resonance imaging 2019-10, Vol.62, p.46-56
Main Authors: Jiřík, Radovan, Taxt, Torfinn, Macíček, Ondřej, Bartoš, Michal, Kratochvíla, Jiří, Souček, Karel, Dražanová, Eva, Krátká, Lucie, Hampl, Aleš, Starčuk, Zenon
Format: Article
Language:English
Subjects:
Algorithms
Animals
Arterial input function
Arteries - diagnostic imaging
Blind deconvolution
Computer Simulation
Contrast Media - pharmacokinetics
DCE-MRI
Humans
Image Processing, Computer-Assisted - methods
Magnetic Resonance Imaging
Mice
Mice, Inbred BALB C
Necrosis - pathology
Perfusion
Pharmacokinetics
Regression Analysis
Reproducibility of Results
Signal-To-Noise Ratio
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Summary:One of the main obstacles for reliable quantitative dynamic contrast-enhanced (DCE) MRI is the need for accurate knowledge of the arterial input function (AIF). This is a special challenge for preclinical small animal applications where it is very difficult to measure the AIF without partial volume and flow artifacts. Furthermore, using advanced pharmacokinetic models (allowing estimation of blood flow and permeability-surface area product in addition to the classical perfusion parameters) poses stricter requirements on the accuracy and precision of AIF estimation. This paper addresses small animal DCE-MRI with advanced pharmacokinetic models and presents a method for estimation of the AIF based on blind deconvolution. A parametric AIF model designed for small animal physiology and use of advanced pharmacokinetic models is proposed. The parameters of the AIF are estimated using multichannel blind deconvolution. Evaluation on simulated data show that for realistic signal to noise ratios blind deconvolution AIF estimation leads to comparable results as the use of the true AIF. Evaluation on real data based on DCE-MRI with two contrast agents of different molecular weights showed a consistence with the known effects of the molecular weight. Multi-channel blind deconvolution using the proposed AIF model specific for small animal DCE-MRI provides reliable perfusion parameter estimates under realistic signal to noise conditions.
ISSN:0730-725X
1873-5894
DOI:10.1016/j.mri.2019.05.024