Adaptability index: quantifying CT tube current modulation performance from dose and quality informatics

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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. © (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.






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Francesco Ria

Assistant Professor of Radiology

Dr. Francesco Ria is a medical physicist and he serves as an Assistant Professor in the Department of Radiology. Francesco has an extensive expertise in the assessment of procedure performances in radiology. In particular, his research activities focus on the simultaneous evaluation of radiation dose and image quality in vivo in computed tomography providing a comprehensive evaluation of radiological exams. Moreover, Francesco is developing and investigating novel mathematical models that, uniquely in the radiology field, can incorporate a comprehensive and quantitative risk-to-benefit assessment of the procedures; he is continuing to apply his expertise towards the definition of new patient specific risk metrics, and in the assessment of image quality in vivo also using state-of-the-art imaging technology, such as photon counting computed tomography scanners, and machine learning reconstruction algorithms.

Dr. Ria is a member of the American Association of Physicists in Medicine task group 392 (Investigation and Quality Control of Automatic Exposure Control System in CT), of the American Association of Physicists in Medicine Public Education working group (WGATE), and of the Italian Association of Medical Physics task group Dose Monitoring in Diagnostic Imaging.


Joshua Wilson

Assistant Professor of Radiology

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