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{Whittington:2021:10.2967/jnumed.120.258962,
author = {Whittington, A and Gunn, RN and Alzheimers, Disease Neuroimaging Initiative},
doi = {10.2967/jnumed.120.258962},
journal = {Journal of Nuclear Medicine},
pages = {1292--1300},
title = {TauIQ: a canonical image based algorithm to quantify tau PET scans.},
url = {http://dx.doi.org/10.2967/jnumed.120.258962},
volume = {62},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Recently, AmyloidIQ was introduced as a new canonical image-based algorithm to quantify amyloid PET scans and demonstrated increased power over traditional SUV ratio (SUVR) approaches when assessed in cross-sectional and longitudinal analyses. We build further on this mathematical framework to develop a TauIQ algorithm for the quantitative analysis of the more complex spatial distribution displayed by tau PET radiotracers. Methods: Cross-sectional (n = 615) and longitudinal (n = 149) 18F-flortaucipir data were obtained from the Alzheimer's Disease Neuroimaging Initiative along with necessary adjunct amyloid PET and T1-weighted structural MRI data. A subset of these data were used to derive a chronological tau dataset, using AmyloidIQ analysis of associated amyloid PET data to calculate the subject's temporal position in the canonical AD disease process, from which canonical images for the nonspecific and specific binding components of 18F-flortaucipir in AD were calculated. These 2 canonical images were incorporated into the TauIQ algorithm that enables the quantification of both global and local tau outcome measures using an image-based regression and statistical parametric analysis of the initial residual image. Performance of the TauIQ algorithm was compared with SUVR approaches for cross-sectional analyses, longitudinal analyses, and correlation with clinical measures (Alzheimer Disease Assessment Scale-Cognitive Subscale [ADAS-Cog], Clinical Dementia Rating scale-sum of boxes [CDR-SB], and Mini-Mental State Examination [MMSE]). Results: TauIQ successfully calculated global tau load (TauL) in all 791 scans analyzed (range, -3.5% to 185.2%; mean ± SD, 23% ± 20.5%) with a nonzero additional local tau component being required in 31% of all scans (cognitively normal [CN], 22%; mild cognitive impairment [MCI], 35%; dementia, 72%). TauIQ was compared with the best SUVR approach in the cross-sectional analysis (TauL increase in effect size: CN- vs. CN+, +
AU  - Whittington,A
AU  - Gunn,RN
AU  - Alzheimers,Disease Neuroimaging Initiative
DO  - 10.2967/jnumed.120.258962
EP  - 1300
PY  - 2021///
SN  - 0161-5505
SP  - 1292
TI  - TauIQ: a canonical image based algorithm to quantify tau PET scans.
T2  - Journal of Nuclear Medicine
UR  - http://dx.doi.org/10.2967/jnumed.120.258962
UR  - https://www.ncbi.nlm.nih.gov/pubmed/33517326
UR  - https://jnm.snmjournals.org/content/62/9/1292/tab-article-info
UR  - http://hdl.handle.net/10044/1/91576
VL  - 62
ER  -
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