Adaptive edge-preserving regularization for PET image reconstruction

Ming Fang; Chien-Min Kao; Ajit Singh

doi:10.1117/12.175059

11 May 1994 Adaptive edge-preserving regularization for PET image reconstruction

Ming Fang, Chien-Min Kao, Ajit Singh

Proceedings Volume 2167, Medical Imaging 1994: Image Processing; (1994) https://doi.org/10.1117/12.175059
Event: Medical Imaging 1994, 1994, Newport Beach, CA, United States

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.

Citation Download Citation

Ming Fang, Chien-Min Kao, and Ajit Singh "Adaptive edge-preserving regularization for PET image reconstruction", Proc. SPIE 2167, Medical Imaging 1994: Image Processing, (11 May 1994); https://doi.org/10.1117/12.175059

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