Regularization for uniform spatial resolution properties in penalized-likelihood image reconstruction.

J W Stayman; J A Fessler

doi:10.1109/42.870666

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Regularization for uniform spatial resolution properties in penalized-likelihood image reconstruction.

J W Stayman·J A Fessler

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Abstract

Traditional space-invariant regularization methods in tomographic image reconstruction using penalized-likelihood estimators produce images with nonuniform spatial resolution properties. The local point spread functions that quantify the smoothing properties of such estimators are space-variant, asymmetric, and object-dependent even for space-invariant imaging systems. We propose a new quadratic regularization scheme for tomographic imaging systems that yields increased spatial uniformity and is motivated by the least-squares fitting of a parameterized local impulse response to a desired global response. We have developed computationally efficient methods for PET systems with shift-invariant geometric responses. We demonstrate the increased spatial uniformity of this new method versus conventional quadratic regularization schemes in simulated PET thorax scans.

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Computer Simulation Humans Image Processing, Computer-Assisted Models, Theoretical Phantoms, Imaging Thorax Tomography, Emission-Computed Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.

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J W Stayman, J A Fessler. 2000. Regularization for uniform spatial resolution properties in penalized-likelihood image reconstruction.. https://doi.org/10.1109/42.870666

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