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Proceedings Paper

Performance evaluation of the filtered back projection reconstruction and the iterative ML reconstruction for PET images
Author(s): Cliff X. Wang; Wesley E. Snyder; Griff L. Bilbro; Peter Santago
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Paper Abstract

The filtered-backprojection (FBP) algorithm and statistical model based iterative algorithms such as the maximum likelihood (ML) reconstruction are the two major classes of tomographic reconstruction method. The FBP method is widely used in clinical setting while iterative methods have attracted research interests in the past decade. In this paper we study the performance of the FBP and the ML methods using simulated projection data. The results indicate that the best image that the FBP or the ML algorithm can generate is the compromise of image smoothness and sharpness. The filter cutoff frequency for the FBP algorithm or the number of iterations for the ML algorithm has to be selected carefully.

Paper Details

Date Published: 16 April 1996
PDF: 8 pages
Proc. SPIE 2710, Medical Imaging 1996: Image Processing, (16 April 1996); doi: 10.1117/12.237995
Show Author Affiliations
Cliff X. Wang, Bowman Gray School of Medicine/Wake Forest Univ. and North Carolina State Univ. (United States)
Wesley E. Snyder, Bowman Gray School of Medicine/Wake Forest Univ. and North Carolina State Univ. (United States)
Griff L. Bilbro, North Carolina State Univ. (United States)
Peter Santago, Bowman Gray School of Medicine/Wake Forest Univ. (United States)


Published in SPIE Proceedings Vol. 2710:
Medical Imaging 1996: Image Processing
Murray H. Loew; Kenneth M. Hanson, Editor(s)

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