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

• 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
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2004.838324

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.