Development and Evaluation of Image Reconstruction Algorithms for a Novel Desktop SPECT System

Document Type: Original Article

Authors

1 Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran

2 Department of Radiology, Johns Hopkins University, Baltimore, Maryland, US and Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland, US

3 Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran and Research Center for Science and Technology in Medicine, Tehran University of Medical Sciences, Tehran, Iran

4 Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran and Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran

Abstract

Objective (s): Various iterative reconstruction algorithms in nuclear medicine have been introduced in the last three decades. For each new imaging system, it is wise to select appropriate image reconstruction algorithms and evaluate their performance. In this study, three approaches of image reconstruction were developed for a novel desktop open-gantry SPECT system, PERSPECT, to assess their performance in terms of the quality of the resultant reconstructed images.
Methods: In the present work, a proposed image reconstruction algorithm for the PERSPECT, referred to as quasi-simultaneous multiplicative algebraic reconstruction technique (qSMART), together with two popular image reconstruction methods, maximum-likelihood expectation-maximization (MLEM) and ordered-subsets EM (OSEM), were implemented and compared. Analytic and Monte Carlo simulations were applied for data acquisition of various phantoms including a micro-Derenzo phantom. All acquired data were reconstructed by the three algorithms using different number of iterations (1-40). A thorough set of figures-of-merit was utilized to quantitatively compare the generated images.
Results: OSEM depicted reconstructed images of higher (or matching) quality in comparison to qSMART. MLEM also reached nearly similar quality as OSEM but at higher number of iterations. The graph of data discrepancy revealed that the ranking of the three approaches in terms of convergence speed is as qSMART, OSEM, and MLEM. Furthermore, bias-versus-noise curves indicated that optimal bias-noise results were achieved using OSEM.
Conclusion: The results showed that although qSMART can be applied for image reconstruction if being halted in the early iterations (up to 5), the best achievable quality of images is obtained using the OSEM.

Keywords

Main Subjects


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