9 March 2017 Adaptability index: quantifying CT tube current modulation performance from dose and quality informatics
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Abstract
The balance between risk and benefit in modern CT scanners is governed by the automatic adaptation mechanisms that adjust x-ray flux for accommodating patient size to achieve certain image noise values. The effectiveness of this adaptation is an important aspect of CT performance and should ideally be characterized in the context of real patient cases. Objective of this study was to characterize CT performance with an index that includes image-noise and radiation dose across a clinical patient population. The study included 1526 examinations performed by three scanners, from two vendors, used for two clinical protocols (abdominopelvic and chest). The dose-patient size and noise-patient size dependencies were linearized, and a 3D-fit was performed for each protocol and each scanner with a planar function. In the fit residual plots the Root Mean Square Error (RMSE) values were estimated as a metric of CT adaptability across the patient population. The RMSE values were between 0.0344 HU1/2 and 0.0215 HU1/2: different scanners offer varying degrees of reproducibility of noise and dose across the population. This analysis could be performed with phantoms, but phantom data would only provide information concerning specific exposure parameters for a scan: instead, a general population comparison is a way to obtain new information related to the relevant clinical adaptability of scanner models. A theoretical relationship between image noise, CTDIvol and patient size was determined based on real patient data. This relationship may provide a new index related to the scanners' adaptability concerning image quality and radiation dose across a patient population.
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F. Ria, F. Ria, J. M. Wilson, J. M. Wilson, Y. Zhang, Y. Zhang, E. Samei, E. Samei, } "Adaptability index: quantifying CT tube current modulation performance from dose and quality informatics", Proc. SPIE 10132, Medical Imaging 2017: Physics of Medical Imaging, 101322N (9 March 2017); doi: 10.1117/12.2255631; https://doi.org/10.1117/12.2255631
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