CT perfusion imaging of the liver enables the evaluation of perfusion metrics that can reveal hepatic diseases and that can be used to assess treatment responses. However, x-ray radiation dose limits more widespread adoption of liver CT perfusion studies as a diagnostic tool. In this work we assess a model-based reconstruction method called Reconstruction of Difference (RoD) for use in low-dose CT perfusion of the liver. The RoD approach integrates a baseline non-contrast-enhanced scan into the reconstruction objective to improve image volumes formed from low- exposure data. Simulation studies were conducted using a digital human liver phantom based on segmented anthropomorphic CT images and time-attenuation curves derived from CT perfusion studies in a rabbit model. We compare the RoD method with standard FBP and penalized-likelihood reconstructions through an evaluation of individual image volumes in the low-dose enhanced liver volume sequence as well as in an evaluation of perfusion maps. Specific perfusion metrics include hepatic arterial perfusion (HAP), hepatic portal perfusion (HPP) and perfusion index (PI) parameters computed using the dual-input maximum slope method (SM). The quantitative and qualitative comparisons of reconstructed images and perfusion maps shows that the RoD approach can significantly reduce noise in low-dose acquisitions while maintaining accurate hepatic perfusion maps as compared with traditional reconstruction methods.     «

CT perfusion imaging of the liver enables the evaluation of perfusion metrics that can reveal hepatic diseases and that can be used to assess treatment responses. However, x-ray radiation dose limits more widespread adoption of liver CT perfusion studies as a diagnostic tool. In this work we assess a model-based reconstruction method called Reconstruction of Difference (RoD) for use in low-dose CT perfusion of the liver. The RoD approach integrates a baseline non-contrast-enhanced scan into the...     »