EECS CSPL SEMINAR SERIES
WINTER TERM 1999

J. Web Stayman

EECS Department

University of Michigan

stayman@eecs.umich.edu

Thursday, March 25
4:30 - 5:30 PM
Room 1001 EECS

Regularization methods for 3D PET

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 local smoothing properties of such estimators are not only space-variant and asymmetric, but are also object-dependent even for space-invariant imaging systems. We propose a new quadratic regularization scheme for shift-invariant tomographic imaging systems that yields increased spatial uniformity based on least-squares fitting of a parameterized local impulse response to a desired global response. We demonstrate the increased spatial uniformity of this new method versus conventional regularization schemes through an investigation of local point spread functions, and through reconstruction of experimental thorax phantom data with known physical structure.

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