Iterative, direct LOR PET image reconstruction of human brain data for the Siemens mMR Biograph

J. J. Scheins, J. Baran, C. Lerche, N. J. Shah, Z. Chen, G. Egan

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

Abstract

In this paper we present first results of an implementation of a direct LOR PET image reconstruction for the Siemens mMR Biograph. The implementation is based on a speed-optimised version of the PET Reconstruction Software Toolkit (PRESTO) to obtain moderate reconstruction time on a single computer. Reconstructed images for Iida phantom data and FDG human brain data are compared to images of the available Siemens mMR Biograph PSF reconstruction. Images of the Siemens PSF reconstruction suffer from Gibbs artifacts whereas for the direct LOR reconstruction no evident Gibbs artifacts are observed. For this reason, the direct LOR reconstruction can effectively improve quantification accuracy due to the reduction of the bias evoked by applying physical PSF kernels.

Original languageEnglish
Title of host publication2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) - Conference Proceedings
Place of PublicationDanvers MA USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages2
ISBN (Electronic)9781538622827
DOIs
Publication statusPublished - 2017
EventIEEE Nuclear Science Symposium and Medical Imaging Conference 2017 - Atlanta, United States of America
Duration: 21 Oct 201728 Oct 2017
https://ieeexplore.ieee.org/document/8038274

Conference

ConferenceIEEE Nuclear Science Symposium and Medical Imaging Conference 2017
Abbreviated titleNSS/MIC 2017
CountryUnited States of America
CityAtlanta
Period21/10/1728/10/17
Internet address

Cite this

Scheins, J. J., Baran, J., Lerche, C., Shah, N. J., Chen, Z., & Egan, G. (2017). Iterative, direct LOR PET image reconstruction of human brain data for the Siemens mMR Biograph. In 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) - Conference Proceedings [8533016] IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/NSSMIC.2017.8533016