Image properties of various ML-based reconstructions of very noisy HRRT data

Simon Stute*, Johan Nuyts, Katrien Van Slambrouck, Mérence Sibomana, Floris Van Velden, Ronald Boellaard, Claude Comtat

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

The use of iterative image reconstruction algorithms with resolution modeling allows for reduced partial volume effect without noise increase. However, it is now recognized that EM-ML type algorithms are biased in very low counts images, in particular for cold regions. Alternative ML algorithms that allow for negative image voxels have been proposed to reduce the bias: NEG-ML of Nuyts et al and AB-ML of Byrne.

Original languageEnglish
Title of host publication2011 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2011
Pages4311-4315
Number of pages5
DOIs
Publication statusPublished - 26 Mar 2012
Event2011 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2011 - Valencia, Spain
Duration: 23 Oct 201129 Oct 2011

Conference

Conference2011 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2011
CountrySpain
CityValencia
Period23/10/201129/10/2011

Cite this

Stute, S., Nuyts, J., Van Slambrouck, K., Sibomana, M., Van Velden, F., Boellaard, R., & Comtat, C. (2012). Image properties of various ML-based reconstructions of very noisy HRRT data. In 2011 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2011 (pp. 4311-4315). [6153830] https://doi.org/10.1109/NSSMIC.2011.6153830