0490728 20240103220156.220180627235959.9 85048278722 000450908300143 10.1007/978-981-10-9035-6_143 5 s. P cav_un_epca*0492788978-981-10-9034-91680-0737World Congress on Medical Physics and Biomedical Engineering, WC 2018777-781SingaporeSpringer2019 DCE-MRI Tissue homogeneity model Tracer kinetic modelling cav_un_auth*0312355 Bartoš Michal UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0108377 Šorel Michal UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0277120 Jiřík Radovan UPT-D D3: Magnetická rezonance a Kryogenika D3: Magnetic Resonance and Cryogenics Magnetic Resonance and Cryogenics Ústav přístrojové techniky AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2018/ZOI/bartos-0490728.pdf cav_un_auth*0359895 MSM100751802 AV ČR CZ cav_un_auth*0338628 GA16-13830S GA ČR CZ Dynamic contrast-enhanced magnetic resonance imaging obtains information about tissue perfusion and permeability. Following the administration of a contrast agent, concentration-time curves measured in each voxel are fitted by a pharmacokinetic model formulated as a time-domain convolution of an arterial input function (AIF) and an impulse residue function (IRF). Since the measurement window contains hundreds of time samples, the discrete convolution is demanding, even when it is performed via discrete Fourier transform (DFT). Additionally, its discretization causes convergence complications in the curve fitting and it is not applicable to functions without a closed-form expression in the time domain, e.g. tissue homogeneity model IRF. Both issues can be solved by formulating the functions in a closed form in the Fourier domain. In the Fourier domain, the model transforms to multiplication of IRF and AIF, followed by the inverse DFT. To avoid time-domain aliasing, the number of samples in the Fourier domain must be higher than the sum of supports of the functions in the time domain. If the functions are slowly decaying exponentials, the support is theoretically infinite, which dramatically reduces the computational performance. In this contribution, we propose a modification of IRF in the Fourier domain to consider the measurement window. Our solution reduces the required number of samples to three times the measurement window compared to dozens needed without the modification and reduces the number of DFTs. This provides faster evaluation of the pharmacokinetic model and its derivatives for each voxel in each iteration of the curve fitting. cav_un_auth*0361984 World Congress on Medical Physics and Biomedical Engineering 20180603 20180608 Praha CZ JC 10000 10200 10201 2020 SCOPUS 3 UPT-D 10000 10200 10201 UPT-D JD RVO:67985556 RVO:68081731 http://hdl.handle.net/11104/0285273 S 2 Article Geosciences Multidisciplinary 0.143 Q4 2019 85048278722 SCOPUS PUBMED 000450908300143 WOS cav_un_epca*0492788 IFMBE Proceedings, Volume 68, Issue 1 World Congress on Medical Physics and Biomedical Engineering, WC 2018 978-981-10-9034-9 1680-0737 777 781 Singapore Springer 2019