Estimation of in vivo noise in clinical CT images: comparison and validation of three different methods against ensemble noise gold-standard
Abstract
Image quality estimation is crucial in modern CT with noise magnitude playing a key
role. Several methods
have been proposed to estimate noise surrogates in vivo. This study aimed to ascertain
the accuracy of
three different noise-magnitude estimation methods. We used ensemble noise as the
ground truth. The
most accurate approach to assess ensemble noise is to scan a patient repeatedly and
assess the noise for
each pixel across the ensemble of images. This process is ethically undoable on actual
patients. In this
study, we surmounted this impasse using Virtual Imaging Trials (VITs) that simulate
clinical scenarios using
computer-based simulations. XCAT phantoms were imaged 47 times using a scanner-specific
simulator
(DukeSim) and reconstructed with filtered back projection (FBP) and iterative (IR)
algorithms. Noise
magnitudes were calculated in lung (ROIn), soft tissues (GNI), and air surrounding
the patient (AIRn),
applying different HU thresholds and techniques. The results were compared with the
ensemble noise
magnitudes within soft tissue (En). For the FBP-reconstructed images, median En was
30.6 HU; median
ROIn was 46.6 HU (+52%), median GNI was 40.1 HU (+31%), and median AIRn 25.1 HU (-18%).
For the IR
images, median En was 19.5 HU; median ROIn was 31.2 HU (+60%), median GNI was 25.1
HU (+29%), and
median AIRn 18.8 HU (-4%). Compared to ensemble noise, GNI and ROIn overestimate the
tissue noise,
while AIRn underestimates it. Air noise was least representative of variations in
tissue noise due to imaging
condition. These differences may be applied as adjustment or calibration factors to
better represent
clinical results.
Type
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https://hdl.handle.net/10161/22422Collections
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Show full item recordScholars@Duke
Ehsan Abadi
Assistant Professor in Radiology
Ehsan Abadi, PhD is an imaging scientist at Duke University. He serves as an Assistant
Professor in the departments of Radiology and Electrical & Computer Engineering, a
faculty member in the Medical Physics Graduate Program and Carl E. Ravin Advanced
Imaging Laboratories, and a co-Lead in the Center for Virtual Imaging Trials. Ehsan’s
research focuses on quantitative imaging and optimization, CT imaging, lung diseases,
computational human modeling, and medical imag
Francesco Ria
Assistant Professor in the Department 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
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