Comparison between ML-EM and modified Newton algorithms for SPECT image reconstruction

Rita Noumeir; Guy E. Mailloux; Hail Mallouche; Raymond Lemieux

doi:10.1117/12.151187

27 August 1993 Comparison between ML-EM and modified Newton algorithms for SPECT image reconstruction

Rita Noumeir, Guy E. Mailloux, Hail Mallouche, Raymond Lemieux

Proceedings Volume 1887, Physiological Imaging, Spectroscopy, and Early-Detection Diagnostic Methods; (1993) https://doi.org/10.1117/12.151187
Event: OE/LASE'93: Optics, Electro-Optics, and Laser Applications in Scienceand Engineering, 1993, Los Angeles, CA, United States

Abstract

The expectation maximization method for maximum likelihood image reconstruction (ML- EM) is one of the most popular algorithms used in SPECT and PET, because it is based on the realistic assumption that photon emission and counts follow a Poisson process. Moreover, this method retains two important theoretical and practical properties namely nonnegativity and self-normalization of the reconstructed image. This latter property means that the number of emitted photons is equal to the number of counts. However, the major disadvantage of this method is the large amount of computation that is required, due to its slow rate of convergence. In this paper, we demonstrate that the ML-EM algorithm is a special case of the modified Newton method and can thus be accelerated by multiplying at each iteration the changes to the image, as calculated by the standard algorithm, by an overrelaxation parameter. This accelerated ML-EM algorithm can further be optimally accelerated, and converges to a good maximum likelihood estimator.

Citation Download Citation

Rita Noumeir, Guy E. Mailloux, Hail Mallouche, and Raymond Lemieux "Comparison between ML-EM and modified Newton algorithms for SPECT image reconstruction", Proc. SPIE 1887, Physiological Imaging, Spectroscopy, and Early-Detection Diagnostic Methods, (27 August 1993); https://doi.org/10.1117/12.151187

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available