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Van Horn JD. Editorial: On the Economics of Neuroscientific Data Sharing. Neuroinformatics 2024; 22:1-4. [PMID: 37966621 DOI: 10.1007/s12021-023-09649-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Affiliation(s)
- John Darrell Van Horn
- Department of Psychology, University of Virginia, Charlottesville, VA, USA.
- School of Data Science, University of Virginia, Charlottesville, VA, USA.
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Chen Z, Sforazzini F, Baran J, Close T, Shah NJ, Egan GF. MR-PET head motion correction based on co-registration of multicontrast MR images. Hum Brain Mapp 2019; 42:4081-4091. [PMID: 30604898 DOI: 10.1002/hbm.24497] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Revised: 10/22/2018] [Accepted: 12/05/2018] [Indexed: 01/01/2023] Open
Abstract
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.
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Affiliation(s)
- Zhaolin Chen
- Monash Biomedical Imaging, Monash University, Melbourne, Australia.,Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, Australia
| | | | - Jakub Baran
- Monash Biomedical Imaging, Monash University, Melbourne, Australia.,Department of Biophysics, Faculty of Mathematics and Natural Sciences, University of Rzesow, Rzesow, Poland
| | - Thomas Close
- Monash Biomedical Imaging, Monash University, Melbourne, Australia.,Australian National Imaging Facility, St Lucia, Australia
| | - Nadim Jon Shah
- Monash Biomedical Imaging, Monash University, Melbourne, Australia.,Institute of Neuroscience and Medicine - 4, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Gary F Egan
- Monash Biomedical Imaging, Monash University, Melbourne, Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Monash University, Clayton, Australia.,Monash Institute of Cognitive and Clinical Neuroscience, Monash University, Melbourne, Australia
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