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Proceedings Paper

Adaptive edge-preserving regularization for PET image reconstruction
Author(s): Ming Fang; Chien-Min Kao; Ajit Singh
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Paper Abstract

We describe an adaptive regularization scheme and show how to incorporate it into either the Algebraic Reconstruction Technique (ART) or Maximum Likelihood-Expectation Maximization (ML-EM) based algorithms for reconstruction of Positron Emission Tomography (PET) images. We demonstrate through qualitative and quantitative experiments that the adaptive regularization technique effectively reduces the noise level in the image, while preserving the fine details of the edge structures in the image. The technique does not introduce any visible artifacts during reconstruction.

Paper Details

Date Published: 11 May 1994
PDF: 12 pages
Proc. SPIE 2167, Medical Imaging 1994: Image Processing, (11 May 1994); doi: 10.1117/12.175059
Show Author Affiliations
Ming Fang, Siemens Corporate Research, Inc. (United States)
Chien-Min Kao, Univ. of Chicago (United States)
Ajit Singh, Siemens Corporate Research, Inc. (United States)


Published in SPIE Proceedings Vol. 2167:
Medical Imaging 1994: Image Processing
Murray H. Loew, Editor(s)

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