0466408 20240103213046.920161206235959.9 Using multichannel blind AIF estimation with the DCATH model to allow increased temporal sampling interval in DCE-MRI 2 s. E cav_un_epca*0466472MagmaMagma29 S1BerlinSpringer2016 DCE-MRI AIF cav_un_auth*0306308 Kratochvíla Jiří D3: Magnetická rezonance a Kryogenika D3: Magnetic Resonance and Cryogenics UPT-D Magnetic Resonance and Cryogenics Ústav přístrojové techniky AV ČR, v. v. i. cav_un_auth*0277120 Jiřík Radovan D3: Magnetická rezonance a Kryogenika D3: Magnetic Resonance and Cryogenics UPT-D Magnetic Resonance and Cryogenics Ústav přístrojové techniky AV ČR, v. v. i. cav_un_auth*0101622 Starčuk jr. Zenon D3: Magnetická rezonance a Kryogenika D3: Magnetic Resonance and Cryogenics UPT-D Magnetic Resonance and Cryogenics Ústav přístrojové techniky AV ČR, v. v. i. cav_un_auth*0312355 Bartoš Michal Zpracování obrazové informace Department of Image Processing ZOI ZOI UTIA-B Department of Image Processing CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0278224 Standara M. CZ cav_un_auth*0277119 Taxt T. NO http://link.springer.com/journal/10334/29/1/suppl/page/1 cav_un_auth*0338628 GA16-13830S GA ČR CZ cav_un_auth*0303710 LO1212 GA MŠk CZ Dynamic contrast-enhanced (DCE) MRI is used for estimation of tissue perfusion parameters, mainly in oncology. Quantitative DCE-MRI analysis requires knowledge of the arterial input function (AIF). It can be measured from a feeding artery, eventually averaged over a population. This leads to partialvolume and flow artifacts, ignored dispersion terms and variability among patients. Multichannel blind deconvolution is an alternative approach avoiding these problems. The AIF is estimated from several tissue tracer concentration curves. Here, blind deconvolution with a parametric AIF model is used to allow lower temporal resolution in DCE-MRI acquisition, while maintaining the accuracy of perfusion analysis. Compared to, a more complex (higher sampling demands) model for impulse residue function (IRF), DCATH, is used. cav_un_auth*0338580 ESMRMB 2016 Congress 20160929 20161001 Vienna AT BM 2017 6 PR UTIA-B BM RVO:68081731 RVO:67985556 http://hdl.handle.net/11104/0264777 cav_un_auth*0296535 Ústav teorie informace a automatizace AV ČR ÚTIA AV ČR CZ S 2016 SCOPUS PUBMED WOS cav_un_epca*0466472 Magma 29 S1 1352-8661 S288-S289 Berlin Springer 2016