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Iterative three‐dimensional expectation maximization restoration of single photon emission computed tomography images: Application in striatal imaging
Author(s) -
Gantet Pierre,
Payoux Pierre,
Celler Anna,
Majorel Cynthia,
Gourion Daniel,
Noll Dominikus,
Esquerré JeanPaul
Publication year - 2006
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.2135908
Subject(s) - iterative reconstruction , expectation–maximization algorithm , contrast (vision) , maximum a posteriori estimation , iterative method , noise (video) , image resolution , imaging phantom , computer science , point spread function , artificial intelligence , emission computed tomography , algorithm , statistical noise , maximization , pattern recognition (psychology) , mathematics , physics , optics , positron emission tomography , maximum likelihood , statistics , nuclear medicine , mathematical optimization , image (mathematics) , medicine , machine learning
Single photon emission computed tomography imaging suffers from poor spatial resolution and high statistical noise. Consequently, the contrast of small structures is reduced, the visual detection of defects is limited and precise quantification is difficult. To improve the contrast, it is possible to include the spatially variant point spread function of the detection system into the iterative reconstruction algorithm. This kind of method is well known to be effective, but time consuming. We have developed a faster method to account for the spatial resolution loss in three dimensions, based on a postreconstruction restoration method. The method uses two steps. First, a noncorrected iterative ordered subsets expectation maximization (OSEM) reconstruction is performed and, in the second step, a three‐dimensional (3D) iterative maximum likelihood expectation maximization (ML‐EM) a posteriori spatial restoration of the reconstructed volume is done. In this paper, we compare to the standard OSEM‐3D method, in three studies (two in simulation and one from experimental data). In the two first studies, contrast, noise, and visual detection of defects are studied. In the third study, a quantitative analysis is performed from data obtained with an anthropomorphic striatal phantom filled with 123‐I. From the simulations, we demonstrate that contrast as a function of noise and lesion detectability are very similar for both OSEM‐3D and OSEM‐R methods. In the experimental study, we obtained very similar values of activity‐quantification ratios for different regions in the brain. The advantage of OSEM‐R compared to OSEM‐3D is a substantial gain of processing time. This gain depends on several factors. In a typical situation, for a 128 × 128 acquisition of 120 projections, OSEM‐R is 13 or 25 times faster than OSEM‐3D, depending on the calculation method used in the iterative restoration. In this paper, the OSEM‐R method is tested with the approximation of depth independent resolution. For the striatum this approximation is appropriate, but for other clinical situations we will need to include a spatially varying response. Such a response is already included in OSEM‐3D.