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