
Proceedings Paper
2D deconvolution of SPECT images using morphological informationFormat | Member Price | Non-Member Price |
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Paper Abstract
Single photon emission computed tomography (SPECT) images are significantly degraded by scattering, attenuation, and intrinsic resolution of collimator. The result is a loss of image quality and quantitative accuracy. These images can still give some useful information, when compared with MR or CT images, so that some patients are periodically monitored by morphological (MR or CT) and functional (SPECT) investigations and these images are matched to determine a space correlation. A method to deconvolve the SPECT images, using the 'a priori' information coming from morphological images, is proposed. This information may be seen as a deterministic constraint to be imposed on the solution of the deconvolution problem and incorporated in the numerical algorithms using projection operators.
Paper Details
Date Published: 9 October 1995
PDF: 9 pages
Proc. SPIE 2570, Experimental and Numerical Methods for Solving Ill-Posed Inverse Problems: Medical and Nonmedical Applications, (9 October 1995); doi: 10.1117/12.224158
Published in SPIE Proceedings Vol. 2570:
Experimental and Numerical Methods for Solving Ill-Posed Inverse Problems: Medical and Nonmedical Applications
Randall Locke Barbour; Mark J. Carvlin; Michael A. Fiddy, Editor(s)
PDF: 9 pages
Proc. SPIE 2570, Experimental and Numerical Methods for Solving Ill-Posed Inverse Problems: Medical and Nonmedical Applications, (9 October 1995); doi: 10.1117/12.224158
Show Author Affiliations
Mario Bertero, Univ. di Genova (Italy)
Patrizia Boccacci, Univ. di Genova (Italy)
D. Brunengo, Univ. di Genova (Italy)
Patrizia Boccacci, Univ. di Genova (Italy)
D. Brunengo, Univ. di Genova (Italy)
Monica Gambaro, National Institute of Cancer Research (Italy)
Andrea Schenone, National Institute of Cancer Research (Italy)
Andrea Schenone, National Institute of Cancer Research (Italy)
Published in SPIE Proceedings Vol. 2570:
Experimental and Numerical Methods for Solving Ill-Posed Inverse Problems: Medical and Nonmedical Applications
Randall Locke Barbour; Mark J. Carvlin; Michael A. Fiddy, Editor(s)
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