Deep residual inception encoder‐decoder network for amyloid PET harmonization

Introduction Multiple positron emission tomography (PET) tracers are available for amyloid imaging, posing a significant challenge to consensus interpretation and quantitative analysis. We accordingly developed and validated a deep learning model as a harmonization strategy. Method A Residual Incept...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Alzheimer's & dementia Ročník 18; číslo 12; s. 2448 - 2457
Hlavní autori: Shah, Jay, Gao, Fei, Li, Baoxin, Ghisays, Valentina, Luo, Ji, Chen, Yinghua, Lee, Wendy, Zhou, Yuxiang, Benzinger, Tammie L.S., Reiman, Eric M., Chen, Kewei, Su, Yi, Wu, Teresa
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States 01.12.2022
Predmet:
Alzheimer Disease - diagnostic imaging
Alzheimer's disease
Amyloid - metabolism
amyloid PET
Amyloidogenic Proteins
Aniline Compounds
Brain - diagnostic imaging
Brain - metabolism
Centiloid
Humans
Positron-Emission Tomography - methods
Radiopharmaceuticals
Alzheimer's disease
Centiloid
amyloid PET
ISSN:1552-5260, 1552-5279, 1552-5279
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Introduction Multiple positron emission tomography (PET) tracers are available for amyloid imaging, posing a significant challenge to consensus interpretation and quantitative analysis. We accordingly developed and validated a deep learning model as a harmonization strategy. Method A Residual Inception Encoder‐Decoder Neural Network was developed to harmonize images between amyloid PET image pairs made with Pittsburgh Compound‐B and florbetapir tracers. The model was trained using a dataset with 92 subjects with 10‐fold cross validation and its generalizability was further examined using an independent external dataset of 46 subjects. Results Significantly stronger between‐tracer correlations (P < .001) were observed after harmonization for both global amyloid burden indices and voxel‐wise measurements in the training cohort and the external testing cohort. Discussion We proposed and validated a novel encoder‐decoder based deep model to harmonize amyloid PET imaging data from different tracers. Further investigation is ongoing to improve the model and apply to additional tracers.
Bibliografia:Yi Su and Teresa Wu contributed equally to this work.
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1552-5260
1552-5279
1552-5279
DOI:10.1002/alz.12564