OBJECTIVE: An extension of single- and multi-channel blind deconvolution is presented to improve the estimation of the arterial input function (AIF) in quantitative dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). METHODS: The Lucy-Richardson expectation-maximization algorithm is used to obtain estimates of the AIF and the tissue residue function (TRF). In the first part of the algorithm, nonparametric estimates of the AIF and TRF are obtained. In the second part, the decaying part of the AIF is approximated by three decaying exponential functions with the same delay, giving an almost noise free semi-parametric AIF. Simultaneously, the TRF is approximated using the adiabatic approximation of the Johnson-Wilson (aaJW) pharmacokinetic model. RESULTS: In simulations and tests on real data, use of this AIF gave perfusion values close to those obtained with the corresponding previously published nonparametric AIF, and are more noise robust. CONCLUSION: When used subsequently in voxelwise perfusion analysis, these semi-parametric AIFs should give more correct perfusion analysis maps less affected by recording noise than the corresponding nonparametric AIFs, and AIFs obtained from arteries. SIGNIFICANCE: This paper presents a method to increase the noise robustness in the estimation of the perfusion parameter values in DCE-MRI.

Czech Academy of Sciences Inst of Scientific Instruments Královopolská 147 Brno 61264 Czech Republic

Dept of Biomedicine University of Bergen Jonas Lies vei 91 Bergen N 5020 Norway

Dept of Biomedicine University of Bergen Jonas Lies vei 91 Bergen N 5020 Norway; Centre for Cancer Biomarkers University of Bergen Jonas Lies vei 87 Bergen N 5021 Norway

Dept of Biomedicine University of Bergen Jonas Lies vei 91 Bergen N 5020 Norway; Dept of Radiology Haukeland University Hospital Jonas Lies vei 83 Bergen N 5020 Norway

Dept of Clinical Engineering Haukeland University Hospital Jonas Lies vei 83 Bergen N 5020 Norway

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