Statistical image reconstruction for polyenergetic X-ray computed tomography.

Idris A Elbakri; Jeffrey A Fessler

doi:10.1109/42.993128

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Statistical image reconstruction for polyenergetic X-ray computed tomography.

Idris A Elbakri·Jeffrey A Fessler

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Abstract

This paper describes a statistical image reconstruction method for X-ray computed tomography (CT) that is based on a physical model that accounts for the polyenergetic X-ray source spectrum and the measurement nonlinearities caused by energy-dependent attenuation. We assume that the object consists of a given number of nonoverlapping materials, such as soft tissue and bone. The attenuation coefficient of each voxel is the product of its unknown density and a known energy-dependent mass attenuation coefficient. We formulate a penalized-likelihood function for this polyenergetic model and develop an ordered-subsets iterative algorithm for estimating the unknown densities in each voxel. The algorithm monotonically decreases the cost function at each iteration when one subset is used. Applying this method to simulated X-ray CT measurements of objects containing both bone and soft tissue yields images with significantly reduced beam hardening artifacts.

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Algorithms Bone and Bones Computer Simulation Humans Image Processing, Computer-Assisted Models, Biological Models, Statistical Monte Carlo Method Phantoms, Imaging Sensitivity and Specificity Tomography, X-Ray Computed Water Comparative Study Journal Article Research Support, U.S. Gov't, P.H.S.

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Idris A Elbakri, Jeffrey A Fessler. 2002. Statistical image reconstruction for polyenergetic X-ray computed tomography.. https://doi.org/10.1109/42.993128

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