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

J. Webster Stayman, Jeffrey A. Fessler

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)601-615
Number of pages15
JournalIEEE transactions on medical imaging
Volume19
Issue number6
DOIs
StatePublished - Jun 2000
Externally publishedYes

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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