Optimized MLAA for quantitative non-TOF PET/MR of the brain

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Didier Benoit, Claes N. Ladefoged, Ahmadreza Rezaei, Sune H. Keller, Flemming L Andersen, Liselotte Højgaard, Adam Espe Hansen, Søren Holm, Johan Nuyts

For quantitative tracer distribution in positron emission tomography, attenuation correction is essential. In a hybrid PET/CT system the CT images serve as a basis for generation of the attenuation map, but in PET/MR, the MR images do not have a similarly simple relationship with the attenuation map. Hence attenuation correction in PET/MR systems is more challenging. Typically either of two MR sequences are used: the Dixon or the ultra-short time echo (UTE) techniques. However these sequences have some well-known limitations. In this study, a reconstruction technique based on a modified and optimized non-TOF MLAA is proposed for PET/MR brain imaging. The idea is to tune the parameters of the MLTR applying some information from an attenuation image computed from the UTE sequences and a T1w MR image. In this MLTR algorithm, an [Formula: see text] parameter is introduced and optimized in order to drive the algorithm to a final attenuation map most consistent with the emission data. Because the non-TOF MLAA is used, a technique to reduce the cross-talk effect is proposed. In this study, the proposed algorithm is compared to the common reconstruction methods such as OSEM using a CT attenuation map, considered as the reference, and OSEM using the Dixon and UTE attenuation maps. To show the robustness and the reproducibility of the proposed algorithm, a set of 204 [(18)F]FDG patients, 35 [(11)C]PiB patients and 1 [(18)F]FET patient are used. The results show that by choosing an optimized value of [Formula: see text] in MLTR, the proposed algorithm improves the results compared to the standard MR-based attenuation correction methods (i.e. OSEM using the Dixon or the UTE attenuation maps), and the cross-talk and the scale problem are limited.

Original languageEnglish
Article number8854
JournalPhysics in Medicine and Biology
Volume61
Issue number24
Number of pages21
ISSN0031-9155
DOIs
Publication statusPublished - 2 Dec 2016

    Research areas

  • Journal Article

ID: 179311489