Blind deconvolution estimation of an arterial input function for small animal DCE-MRI
Jazyk angličtina Země Nizozemsko Médium print-electronic
Typ dokumentu časopisecké články, práce podpořená grantem
PubMed
31150814
DOI
10.1016/j.mri.2019.05.024
PII: S0730-725X(18)30676-3
Knihovny.cz E-zdroje
- Klíčová slova
- Arterial input function, Blind deconvolution, DCE-MRI,
- MeSH
- algoritmy MeSH
- arterie diagnostické zobrazování MeSH
- farmakokinetika MeSH
- kontrastní látky farmakokinetika MeSH
- lidé MeSH
- magnetická rezonanční tomografie * MeSH
- myši inbrední BALB C MeSH
- myši MeSH
- nekróza patologie MeSH
- perfuze MeSH
- počítačová simulace MeSH
- počítačové zpracování obrazu metody MeSH
- poměr signál - šum MeSH
- regresní analýza MeSH
- reprodukovatelnost výsledků MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- myši MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- kontrastní látky MeSH
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.
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