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R Levkovitz

Publications and source records attributed to R Levkovitz.

2 recordsLinked to original sources
PubMed2001

The design and implementation of COSEM, an iterative algorithm for fully 3-D listmode data.

In this paper,we present coincidence-list-ordered sets expectation-maximization (COSEM), an algorithm for iterative image reconstruction directly from list-mode coincidence acquisition data. The COSEM algorithm is based on the ordered sets EM algorithm for binned data but has several extensions that makes it suitable for rotating two planar detector tomographs. We develop the COSEM algorithm and extend it to include analytic calculation of detection probability, noise reducing iterative filtering schemes, and on-the-fly attenuation correction methods. We present an adaptation of COSEM to the Varicam\VG camera and show results from clinical and phantom studies.

Algorithms↗
PubMed2000

Enhanced 3D PET OSEM reconstruction using inter-update Metz filtering.

We present an enhancement of the OSEM (ordered set expectation maximization) algorithm for 3D PET reconstruction, which we call the inter-update Metz filtered OSEM (IMF-OSEM). The IMF-OSEM algorithm incorporates filtering action into the image updating process in order to improve the quality of the reconstruction. With this technique, the multiplicative correction image--ordinarily used to update image estimates in plain OSEM--is applied to a Metz-filtered version of the image estimate at certain intervals. In addition, we present a software implementation that employs several high-speed features to accelerate reconstruction. These features include, firstly, forward and back projection functions which make full use of symmetry as well as a fast incremental computation technique. Secondly, the software has the capability of running in parallel mode on several processors. The parallelization approach employed yields a significant speed-up, which is nearly independent of the amount of data. Together, these features lead to reasonable reconstruction times even when using large image arrays and non-axially compressed projection data. The performance of IMF-OSEM was tested on phantom data acquired on the GE Advance scanner. Our results demonstrate that an appropriate choice of Metz filter parameters can improve the contrast-noise balance of certain regions of interest relative to both plain and post-filtered OSEM, and to the GE commercial reprojection algorithm software.

Algorithms↗