Use of novel iterative reconstruction in CT KUB: approach on improving image quality

Abstract

Purpose: To compare image quality on computed tomographic (CT) images acquired with filtered backprojection (FBP), adaptive statistical iterative reconstruction (ASIR) and model-based iterative reconstruction (MBIR) techniques in CT KUB examination. Methods and Materials: 20 patients were scanned with standard protocol CT KUB at our institution. Same raw data were reconstructed using FBP, ASIR and MBIR. Objective and subjective image qualities were assessed. Quantitative data such as objective image noise and mean attenuation were analysed by comparing standard deviations, 95% confidence interval and calculating percentage difference. Mean image noise values and attenuation values were compared between different reconstruction algorithms using ANOVA. The interobserver variation and percentage agreement between the two radiologists for each of the assessed subjective image quality and lesion assessment parameters were estimated by using weighted k-statistics. Kruskal-Wallis rank sum test was used to test for equality of median scores among all subjective parameters. Results: Objective image analysis supports significant noise reduction and superior contrast-to-noise ratio with new MBIR technique (p < 0.05). Subjective image parameters were maximally rated for MBIR followed by ASIR then FBP and show statistical significance differences (p < 0.05). Conclusion: MBIR shows superior reduction in noise and improved image quality (both objective and subjective analysis) compared with ASIR and FBP in the context of CT KUB scanning which is inherently a noisy study. At same dose, we have shown that image quality can be improved by applying MBIR in clinical practice, and if available should be utilised.