The purpose of this study was to quantitatively evaluate the performance of
different simultaneous spatial and temporal regularizers in liver perfusion
studies using low-dose dynamic contrast-enhanced computed tomography (DCE-CT).
A digital liver phantom was used to simulate chronic liver disease (CLD) and
hepatocellular carcinoma (HCC) based on clinical data. Low-dose DCE-CT images
were reconstructed using regularizers and a primal-dual algorithm.
Subsequently, hepatic perfusion parameter (HPP) images were generated using a
dual-input single-compartment model and linear least-squares method. In the CLD
model, the effect of regularizers on the input functions (IFs) was examined by
calculating the areas under the curves (AUCs) of the IFs, and the HPP
estimation accuracy was evaluated by calculating the error and coefficient of
variation (CV) between the HPP values obtained by the above methods and true
values. In the HCC model, the ratios of the mean HPP values inside and outside
the tumor were calculated. The AUCs of IFs decreased with increasing
regularization parameter (RP) values. Although the AUC of arterial IF did not
significantly depend on the regularizers, that of portal IF did. The error and
CV were reduced using low-rank and sparse decomposition (LRSD). Total
generalized variation (TGV) combined with LRSD (LTGV) was generally superior to
the other regularizers in terms of HPP estimation accuracy and range of
available RP values in both the CLD and HCC models. However, striped artifacts
were more remarkable in the HPP images obtained by the TGV and LTGV than in
those obtained by the other regularizers. The results suggest that the LRSD and
LTGV are useful for improving the accuracy of HPP estimation using low-dose
DCE-CT and for enhancing its practicality. This study will help select a
suitable regularizer and/or RP value for low-dose DCE-CT liver perfusion
studies.Comment: 32 pages, 1 table, 10 figure