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PubMed · 14718146

[Characterization of the difference between filtered back projection and ordered subsets expectation maximization in spect images using the Wavelet Transform].

A Pérez·R Piotrkowski·R Galli·G La Mura·F J Peña
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Abstract

Efficiency of reconstruction algorithms is important to guarantee quality of Nuclear Medicine tomographic images. The algorithms belong to one of two types: analytical or statistical. The Filtered Back Projection (BP) algorithm belongs to the first group and the Ordered Subsets Expectation Maximization (OSEM) algorithm belongs to the second group. The aim of the present paper was to compare both algorithms by means of the Wavelet Transform (WT). This tool was selected because of its capability to divide the image into different frequency levels without losing information about their spatial position. The WT was applied on the Haar basis without decimation in order to compare matrixes of the same size. Attention was drawn to the relative presence of three typical perturbations: Poisson noise, annular artifacts that express a deficient correction to the planar uniformity and attenuation phenomenon. With these purposes, we studied homologue transversal slices of images of homogeneous distribution of activity. The OSEM algorithm not only greatly improves the filtering of the Poisson noise, but also diminishes the annular artifacts, mainly at the image zone that coincides with the rotation center. The intensity of the attenuation phenomenon was the same with both algorithms, except in the peripheral zone where OSEM showed a slightly higher activity than BP. These observations encourage our interest to continue applying the WT not only to identify the artifacts, but also to try to decrease their deleterious influence on Nuclear Medicine images.

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Phantoms, ImagingTomography, Emission-Computed, Single-PhotonEnglish AbstractJournal Article

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A Pérez, R Piotrkowski, R Galli, G La Mura, F J Peña. [Characterization of the difference between filtered back projection and ordered subsets expectation maximization in spect images using the Wavelet Transform].. https://doi.org/10.1016/s0212-6982(04)72240-5

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