Performance evaluation of the filtered back projection reconstruction and the iterative ML reconstruction for PET images

Cliff X. Wang; Wesley E. Snyder; Griff L. Bilbro; Peter Santago II

doi:10.1117/12.237995

16 April 1996 Performance evaluation of the filtered back projection reconstruction and the iterative ML reconstruction for PET images

Cliff X. Wang, Wesley E. Snyder, Griff L. Bilbro, Peter Santago II

Author Affiliations +

Proceedings Volume 2710, Medical Imaging 1996: Image Processing; (1996) https://doi.org/10.1117/12.237995
Event: Medical Imaging 1996, 1996, Newport Beach, CA, United States

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.

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Cliff X. Wang, Wesley E. Snyder, Griff L. Bilbro, and Peter Santago II "Performance evaluation of the filtered back projection reconstruction and the iterative ML reconstruction for PET images", Proc. SPIE 2710, Medical Imaging 1996: Image Processing, (16 April 1996); https://doi.org/10.1117/12.237995

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