Low-Dose CT Perfusion of the Liver using Reconstruction of Difference.

Abstract

Liver CT perfusion (CTP) is used in the detection, staging, and treatment response analysis of hepatic diseases. Unfortunately, CTP radiation exposures is significant, limiting more widespread use. Traditional CTP data processing reconstructs individual temporal samples, ignoring a large amount of shared anatomical information between temporal samples, suggesting opportunities for improved data processing. We adopt a prior-image-based reconstruction approach called Reconstruction of Difference (RoD) to enable low-exposure CTP acquisition. RoD differs from many algorithms by directly estimating the attenuation changes between the current patient state and a prior CT volume. We propose to use a high-fidelity unenhanced baseline CT image to integrate prior anatomical knowledge into subsequent data reconstructions. Using simulation studies based on a 4D digital anthropomorphic phantom with realistic time-attenuation curves, we compare RoD with conventional filtered-backprojection, penalized-likelihood estimation, and prior image penalized-likelihood estimation. We evaluate each method in comparisons of reconstructions at individual time points, accuracy of estimated time-attenuation curves, and in an analysis of common perfusion metric maps including hepatic arterial perfusion, hepatic portal perfusion, perfusion index, and time-to-peak. Results suggest that RoD enables significant exposure reductions, outperforming standard and more sophisticated model-based reconstruction, making RoD a potentially important tool to enable low-dose liver CTP.