Adaptability index: quantifying CT tube current modulation performance from dose and quality informatics
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. © (2017) COPYRIGHT Society of Photo-Optical
Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal
use only.
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https://hdl.handle.net/10161/13861Published Version (Please cite this version)
10.1117/12.2255631Collections
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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 t
Ehsan Samei
Reed and Martha Rice Distinguished Professor of Radiology
Dr. Ehsan Samei, PhD, DABR, FAAPM, FSPIE, FAIMBE, FIOMP, FACR is a Persian-American
medical physicist. He is a tenured Professor of Radiology, Medical Physics, Biomedical
Engineering, Physics, and Electrical and Computer Engineering at Duke University,
where he also serves as the Chief Imaging Physicist for Duke University Health System,
the director of the Carl E Ravin Advanced Imaging Laboratories, and the director of
Center for Virtual Imaging Trials. He is certi
Joshua M Wilson
Assistant Professor of Radiology
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