{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-residual-inception-encoder-decoder-1","title":"Deep residual inception encoder-decoder network for amyloid PET harmonization","arxiv_id":null,"date":"2022-02-09","proceeding":"Alzheimer's and Dementia 2022 2","authors":["Jay Shah","Fei Gao","Baoxin Li","Valentina Ghisays","Ji Luo","Yinghua Chen","Wendy Lee","Yuxiang Zhou","Tammie L.S. Benzinger","Eric M. Reiman","Kewei Chen","Yi Su","Teresa Wu"],"abstract":"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. 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. 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. 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.","url_abs":"https://alz-journals.onlinelibrary.wiley.com/doi/full/10.1002/alz.12564","url_pdf":"https://alz-journals.onlinelibrary.wiley.com/doi/epdf/10.1002/alz.12564","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-residual-inception-encoder-decoder-1","repo_url":"https://github.com/jaygshah/RIED-Net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"image-harmonization","task_name":"Image Harmonization"},{"task_slug":"image-to-image-translation","task_name":"Image-to-Image Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}