22 February 2012 Dose-optimized slice thickness for routine multislice computed tomography liver examinations
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Abstract
The need to optimize CT protocols with respect to radiation dose is widely recognized. This study uses phantom-based methodology to investigate the affect of changes in exposure and slice thickness on observer performance for the detection of low contrast opacities with multislice computed tomography to determine dose- optimized slice thickness and image noise for routine liver imaging. Methods: A phantom containing an opacity with diameter 9.5mm and density 10HU below background was scanned at various exposure and slice thickness settings (range 50-125mAs and 1-3mm). An image set consisting of 120 images containing background-only and 60 images containing the opacity in random locations was created. Following Institutional Review Board approval, nine experienced observers viewed the images and scored opacity visualization using a four-point confidence scale. Noise, contrast-to-noise ratio (CNR), sensitivity, specificity and area under the curve (AUC) were calculated. Comparisons between exposure and slice thickness settings were performed using ROC, Spearman and Wilcoxon techniques. Results: Significant (p<0.05) reductions in AUC and sensitivity occurred when CNR dropped to 0.71 or below and 0.68 or below, respectively. There was strong correlation between noise and AUC (r = -0.79, p<0.01), noise and sensitivity (r = -0.92, p<0.001), CNR and AUC (r = -0.90, p<0.001) and CNR and sensitivity (r = 0.61, p<0.05). Conclusion: Observer performance for the detection of opacities is strongly related to quantum noise and CNR. Dose optimized lesion detection was achieved with 5mm slice thickness and CNR of 0.72 and noise of 9.05.
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K. Dobeli, K. Dobeli, S. Lewis, S. Lewis, S. Meikle, S. Meikle, D. Thiele, D. Thiele, P. C. Brennan, P. C. Brennan, } "Dose-optimized slice thickness for routine multislice computed tomography liver examinations", Proc. SPIE 8318, Medical Imaging 2012: Image Perception, Observer Performance, and Technology Assessment, 831812 (22 February 2012); doi: 10.1117/12.910452; https://doi.org/10.1117/12.910452
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