Precision of iodine quantification in hepatic CT: effects of iterative reconstruction with various imaging parameters.

OBJECTIVE: The objective of this study was to evaluate the feasibility of using iterative reconstructions in hepatic CT to improve the precision of Hounsfield unit quantification, which is the degree to which repeated measurements under unchanged conditions provide consistent results. MATERIALS AND METHODS: An anthropomorphic liver phantom with iodinated lesions designed to simulate the enhancement of hypervascular tumors during the late hepatic arterial phase was imaged, and images were reconstructed with both filtered back projection (FBP) and iterative reconstructions, such as adaptive statistical iterative reconstruction (ASIR) and model-based iterative reconstruction (MBIR). This protocol was further expanded into various dose levels, tube voltages, and slice thicknesses to investigate the effect of iterative reconstructions under all these conditions. The iodine concentrations of the lesions were quantified, with their precision calculated in terms of repeatability coefficient. RESULTS: ASIR reduced image noise by approximately 35%, and improved the quantitative precision by approximately 5%, compared with FBP. MBIR reduced noise by more than 65% and improved the precision by approximately 25% compared with the routine protocol. MBIR consistently showed better precision across a thinner slice thickness, lower tube voltage, and larger patient, achieving the target precision level at a dose lower (≥ 40%) than that of FBP. CONCLUSION: ASIR blended with 50% of FBP indicated a moderate gain in quantitative precision compared with FBP but could achieve more with a higher percentage. A higher gain was achieved by MBIR. These findings may be used to reduce the dose required for reliable quantification and may further serve as a basis for protocol optimization in terms of iodine quantification.

Duke Scholars

Author Ehsan Samei Radiology

Author Daniele Marin Radiology, Abdominal Imaging

Author Huiman Xie Barnhart Biostatistics & Bioinformatics, Division of Biostatistics

Author Rendon C. Nelson Radiology