Reduction of noise-induced streak artifacts in X-ray computed tomography through spline-based penalized-likelihood sinogram smoothing

We present a statistically principled sinogram smoothing approach for X-ray computed tomography (CT) with the intent of reducing noise-induced streak artifacts. These artifacts arise in CT when some subset of the transmission measurements capture relatively few photons because of high attenuation al...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2005-01, Vol.24 (1), p.105-111
Main Authors: La Riviere, P.J., Billmire, D.M.
Format: Article
Language:English
Subjects:
Adaptive filters
Algorithms
Artificial Intelligence
Attenuation measurement
Computed tomography
Computer Simulation
Humans
Information Storage and Retrieval - methods
Models, Biological
Models, Statistical
Noise
Noise level
Noise measurement
Noise reduction
Numerical Analysis, Computer-Assisted
Phantoms, Imaging
Poisson distributions
Radiographic Image Enhancement - methods
Radiographic Image Interpretation, Computer-Assisted - methods
Radiography, Thoracic - instrumentation
Radiography, Thoracic - methods
Reproducibility of Results
Sensitivity and Specificity
Signal Processing, Computer-Assisted
Smoothing methods
Spline
Statistical distributions
Stochastic Processes
Studies
Tomography, X-Ray Computed - instrumentation
Tomography, X-Ray Computed - methods
X-ray imaging
X-ray tomography
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Summary:We present a statistically principled sinogram smoothing approach for X-ray computed tomography (CT) with the intent of reducing noise-induced streak artifacts. These artifacts arise in CT when some subset of the transmission measurements capture relatively few photons because of high attenuation along the measurement lines. Attempts to reduce these artifacts have focused on the use of adaptive filters that strive to tailor the degree of smoothing to the local noise levels in the measurements. While these approaches involve loose consideration of the measurement statistics to determine smoothing levels, they do not explicitly model the statistical distributions of the measurement data. We present an explicitly statistical approach to sinogram smoothing in the presence of photon-starved measurements. It is an extension of a nonparametric sinogram smoothing approach using penalized Poisson-likelihood functions that we have previously developed for emission tomography. Because the approach explicitly models the data statistics, it is naturally adaptive-it will smooth more variable measurements more heavily than it does less variable measurements. We find that it significantly reduces streak artifacts and noise levels without comprising image resolution.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2004.838324