2019
Jiřík, Radovan; Taxt, Torfinn; Macíček, Ondřej; Bartoš, Michal; Kratochvíla, Jiří; Souček, Karel; Dražanová, Eva; Krátká, Lucie; Hampl, Aleš; Starčuk, Zenon Jr
Blind deconvolution estimation of an arterial input function for small animal DCE-MRI. Journal Article
In: Magnetic resonance imaging, vol. 62, pp. 46–56, 2019, ISSN: 1873-5894 0730-725X, (Place: Netherlands).
Abstract | Links | BibTeX | Tags: *Magnetic Resonance Imaging, Algorithms, Animals, Arterial input function, Arteries/*diagnostic imaging, Blind deconvolution, Computer Simulation, Computer-Assisted/*methods, Contrast Media/*pharmacokinetics, DCE-MRI, Humans, Image Processing, Inbred BALB C, Mice, Necrosis/pathology, Perfusion, Pharmacokinetics, Regression Analysis, Reproducibility of Results, Signal-To-Noise Ratio
@article{jirik_blind_2019,
title = {Blind deconvolution estimation of an arterial input function for small animal DCE-MRI.},
author = {Radovan Jiřík and Torfinn Taxt and Ondřej Macíček and Michal Bartoš and Jiří Kratochvíla and Karel Souček and Eva Dražanová and Lucie Krátká and Aleš Hampl and Zenon Jr Starčuk},
doi = {10.1016/j.mri.2019.05.024},
issn = {1873-5894 0730-725X},
year = {2019},
date = {2019-10-01},
journal = {Magnetic resonance imaging},
volume = {62},
pages = {46–56},
abstract = {PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.},
note = {Place: Netherlands},
keywords = {*Magnetic Resonance Imaging, Algorithms, Animals, Arterial input function, Arteries/*diagnostic imaging, Blind deconvolution, Computer Simulation, Computer-Assisted/*methods, Contrast Media/*pharmacokinetics, DCE-MRI, Humans, Image Processing, Inbred BALB C, Mice, Necrosis/pathology, Perfusion, Pharmacokinetics, Regression Analysis, Reproducibility of Results, Signal-To-Noise Ratio},
pubstate = {published},
tppubtype = {article}
}
PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.