Imperial College London
Alternate

ProfessorRogerGunn

Faculty of Medicine, Department of Brain Sciences

Emeritus Professor of Molecular Neuroimaging
 
 
 
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Contact

 

r.gunn

 
 
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Location

 

Burlington DanesHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Veronese:2021:10.1177/0271678X211015101,
author = {Veronese, M and Rizzo, G and Belzunce, M and Schubert, J and Searle, G and Whittington, A and Mansur, A and Dunn, J and Reader, A and Gunn, RN and and, the Grand Challenge Participants},
doi = {10.1177/0271678X211015101},
journal = {Journal of Cerebral Blood Flow and Metabolism},
pages = {2778--2796},
title = {Reproducibility of findings in modern PET neuroimaging: insight from the NRM2018 grand challenge},
url = {http://dx.doi.org/10.1177/0271678X211015101},
volume = {41},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The reproducibility of findings is a compelling methodological problem that the neuroimaging community is facing these days. The lack of standardized pipelines for image processing, quantification and statistics plays a major role in the variability and interpretation of results, even when the same data are analysed. This problem is well-known in MRI studies, where the indisputable value of the method has been complicated by a number of studies that produce discrepant results. However, any research domain with complex data and flexible analytical procedures can experience a similar lack of reproducibility. In this paper we investigate this issue for brain PET imaging. During the 2018 NeuroReceptor Mapping conference, the brain PET community was challenged with a computational contest involving a simulated neurotransmitter release experiment. Fourteen international teams analysed the same imaging dataset, for which the ground-truth was known. Despite a plurality of methods, the solutions were consistent across participants, although not identical. These results should create awareness that the increased sharing of PET data alone will only be one component of enhancing confidence in neuroimaging results and that it will be important to complement this with full details of the analysis pipelines and procedures that have been used to quantify data.
AU  - Veronese,M
AU  - Rizzo,G
AU  - Belzunce,M
AU  - Schubert,J
AU  - Searle,G
AU  - Whittington,A
AU  - Mansur,A
AU  - Dunn,J
AU  - Reader,A
AU  - Gunn,RN
AU  - and,the Grand Challenge Participants
DO  - 10.1177/0271678X211015101
EP  - 2796
PY  - 2021///
SN  - 0271-678X
SP  - 2778
TI  - Reproducibility of findings in modern PET neuroimaging: insight from the NRM2018 grand challenge
T2  - Journal of Cerebral Blood Flow and Metabolism
UR  - http://dx.doi.org/10.1177/0271678X211015101
UR  - https://www.ncbi.nlm.nih.gov/pubmed/33993794
UR  - https://journals.sagepub.com/doi/10.1177/0271678X211015101
UR  - http://hdl.handle.net/10044/1/89209
VL  - 41
ER  -
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