Torfinn Taxt1, Rolf K Reed2, Tina Pavlin1, Cecilie Brekke Rygh3, Erling Andersen4, Radovan Jiřík5. 1. 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. 2. Dept. of Biomedicine, University of Bergen, Jonas Lies vei 91, Bergen N-5020, Norway; Centre for Cancer Biomarkers (CCBIO), University of Bergen, Jonas Lies vei 87, Bergen N-5021, Norway. 3. Dept. of Biomedicine, University of Bergen, Jonas Lies vei 91, Bergen N-5020, Norway. 4. Dept. of Clinical Engineering, Haukeland University Hospital, Jonas Lies vei 83, Bergen N-5020, Norway. 5. Czech Academy of Sciences, Inst. of Scientific Instruments, Královopolská 147, Brno 61264, Czech Republic. Electronic address: jirik@isibrno.cz.
Abstract
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.
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.
Authors: M F Fiordelisi; L Auletta; L Meomartino; L Basso; G Fatone; M Salvatore; M Mancini; A Greco Journal: Contrast Media Mol Imaging Date: 2019-09-22 Impact factor: 3.161
Authors: Daniel Lewis; Damien J McHugh; Ka-Loh Li; Xiaoping Zhu; Catherine Mcbain; Simon K Lloyd; Alan Jackson; Omar N Pathmanaban; Andrew T King; David J Coope Journal: Sci Rep Date: 2021-08-03 Impact factor: 4.379