Anomaly Detection and Artifact Recovery in PET Attenuation-Correction Images Using the Likelihood Function.

Published

Journal Article

In dual modality PET/CT, CT data are used to generate the attenuation correction applied in the reconstruction of the PET emission image. This requires converting the CT image into a 511-keV attenuation map. Algorithms for making this transformation require assumptions about the makeup of material within the patient. Anomalous material such as contrast agent administered to enhance the CT scan confounds conversion algorithms and has been observed to result in inaccuracies, i.e., inconsistencies with the true 511-keV attenuation present at the time of the PET emission scan. These attenuation artifacts carry through to the final attenuation-corrected PET emission image and can resemble diseased tissue. We propose an approach to correcting this problem that employs the attenuation information carried by the PET emission data. A likelihood-based algorithm for identifying and correcting of contrast is presented and tested. The algorithm exploits the fact that contrast artifacts manifest as too-high attenuation values in an otherwise high quality attenuation image. In a separate study, the performance of the loglikelihood as an objective-function component of a detection/correction algorithm, independent of any particular algorithm was mapped out for several imaging scenarios as a function of statistical noise. Both the full algorithm and the loglikelihood performed well in studies with simulated data. Additional studies including those with patient data are required to fully understand their capabilities.

Full Text

Duke Authors

Cited Authors

  • Laymon, CM; Bowsher, JE

Published Date

  • February 2013

Published In

Volume / Issue

  • 7 / 1

PubMed ID

  • 24198866

Pubmed Central ID

  • 24198866

International Standard Serial Number (ISSN)

  • 1932-4553

Digital Object Identifier (DOI)

  • 10.1109/JSTSP.2012.2237380

Language

  • eng

Conference Location

  • United States