Open AccessMR‐PET head motion correction based on co‐registration of multicontrast MR images
Author(s) -
Chen Zhaolin,
Sforazzini Francesco,
Baran Jakub,
Close Thomas,
Shah Nadim Jon,
Egan Gary F.
Publication year - 2021
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.24497
Subject(s) - positron emission tomography , artificial intelligence , computer vision , nuclear medicine , magnetic resonance imaging , computer science , motion (physics) , image registration , radiology , medicine , image (mathematics)
Head motion is a major source of image artefacts in neuroimaging studies and can lead to degradation of the quantitative accuracy of reconstructed PET images. Simultaneous magnetic resonance‐positron emission tomography (MR‐PET) makes it possible to estimate head motion information from high‐resolution MR images and then correct motion artefacts in PET images. In this article, we introduce a fully automated PET motion correction method, MR‐guided MAF, based on the co‐registration of multicontrast MR images. The performance of the MR‐guided MAF method was evaluated using MR‐PET data acquired from a cohort of ten healthy participants who received a slow infusion of fluorodeoxyglucose ([18‐F]FDG). Compared with conventional methods, MR‐guided PET image reconstruction can reduce head motion introduced artefacts and improve the image sharpness and quantitative accuracy of PET images acquired using simultaneous MR‐PET scanners. The fully automated motion estimation method has been implemented as a publicly available web‐service.