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The purpose of this study was to quantitatively evaluate the performance ofdifferent simultaneous spatial and temporal regularizers in liver perfusionstudies using low-dose dynamic contrast-enhanced computed tomography (DCE-CT).A digital liver phantom was used to simulate chronic liver disease (CLD) andhepatocellular carcinoma (HCC) based on clinical data. Low-dose DCE-CT imageswere reconstructed using regularizers and a primal-dual algorithm.Subsequently, hepatic perfusion parameter (HPP) images were generated using adual-input single-compartment model and linear least-squares method. In the CLDmodel, the effect of regularizers on the input functions (IFs) was examined bycalculating the areas under the curves (AUCs) of the IFs, and the HPPestimation accuracy was evaluated by calculating the error and coefficient ofvariation (CV) between the HPP values obtained by the above methods and truevalues. In the HCC model, the ratios of the mean HPP values inside and outsidethe tumor were calculated. The AUCs of IFs decreased with increasingregularization parameter (RP) values. Although the AUC of arterial IF did notsignificantly depend on the regularizers, that of portal IF did. The error andCV were reduced using low-rank and sparse decomposition (LRSD). Totalgeneralized variation (TGV) combined with LRSD (LTGV) was generally superior tothe other regularizers in terms of HPP estimation accuracy and range ofavailable RP values in both the CLD and HCC models. However, striped artifactswere more remarkable in the HPP images obtained by the TGV and LTGV than inthose obtained by the other regularizers. The results suggest that the LRSD andLTGV are useful for improving the accuracy of HPP estimation using low-doseDCE-CT and for enhancing its practicality. This study will help select asuitable regularizer and/or RP value for low-dose DCE-CT liver perfusionstudies.

Quantitative evaluation of simultaneous spatial and temporal regularization in liver perfusion studies using low-dose dynamic contrast-enhanced CT