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Posterior Estimation Using Deep Learning: A Simulation Study of Compartmental Modeling in Dynamic PET
BACKGROUND: In medical imaging, images are usually treated as deterministic, while their uncertainties are largely underexplored. PURPOSE: This work aims at using deep learning to efficiently estimate posterior distributions of imaging parameters, which in turn can be used to derive the most probabl...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cornell University
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055492/ https://www.ncbi.nlm.nih.gov/pubmed/36994161 |
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author | Liu, Xiaofeng Marin, Thibault Amal, Tiss Woo, Jonghye El Fakhri, Georges Ouyang, Jinsong |
author_facet | Liu, Xiaofeng Marin, Thibault Amal, Tiss Woo, Jonghye El Fakhri, Georges Ouyang, Jinsong |
author_sort | Liu, Xiaofeng |
collection | PubMed |
description | BACKGROUND: In medical imaging, images are usually treated as deterministic, while their uncertainties are largely underexplored. PURPOSE: This work aims at using deep learning to efficiently estimate posterior distributions of imaging parameters, which in turn can be used to derive the most probable parameters as well as their uncertainties. METHODS: Our deep learning-based approaches are based on a variational Bayesian inference framework, which is implemented using two different deep neural networks based on conditional variational auto-encoder (CVAE), CVAE-dual-encoder and CVAE-dual-decoder. The conventional CVAE framework, i.e., CVAE-vanilla, can be regarded as a simplified case of these two neural networks. We applied these approaches to a simulation study of dynamic brain PET imaging using a reference region-based kinetic model. RESULTS: In the simulation study, we estimated posterior distributions of PET kinetic parameters given a measurement of time-activity curve. Our proposed CVAE-dual-encoder and CVAE-dual-decoder yield results that are in good agreement with the asymptotically unbiased posterior distributions sampled by Markov Chain Monte Carlo (MCMC). The CVAE-vanilla can also be used for estimating posterior distributions, although it has an inferior performance to both CVAE-dual-encoder and CVAE-dual-decoder. CONCLUSIONS: We have evaluated the performance of our deep learning approaches for estimating posterior distributions in dynamic brain PET. Our deep learning approaches yield posterior distributions, which are in good agreement with unbiased distributions estimated by MCMC. All these neural networks have different characteristics and can be chosen by the user for specific applications. The proposed methods are general and can be adapted to other problems. |
format | Online Article Text |
id | pubmed-10055492 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cornell University |
record_format | MEDLINE/PubMed |
spelling | pubmed-100554922023-03-30 Posterior Estimation Using Deep Learning: A Simulation Study of Compartmental Modeling in Dynamic PET Liu, Xiaofeng Marin, Thibault Amal, Tiss Woo, Jonghye El Fakhri, Georges Ouyang, Jinsong ArXiv Article BACKGROUND: In medical imaging, images are usually treated as deterministic, while their uncertainties are largely underexplored. PURPOSE: This work aims at using deep learning to efficiently estimate posterior distributions of imaging parameters, which in turn can be used to derive the most probable parameters as well as their uncertainties. METHODS: Our deep learning-based approaches are based on a variational Bayesian inference framework, which is implemented using two different deep neural networks based on conditional variational auto-encoder (CVAE), CVAE-dual-encoder and CVAE-dual-decoder. The conventional CVAE framework, i.e., CVAE-vanilla, can be regarded as a simplified case of these two neural networks. We applied these approaches to a simulation study of dynamic brain PET imaging using a reference region-based kinetic model. RESULTS: In the simulation study, we estimated posterior distributions of PET kinetic parameters given a measurement of time-activity curve. Our proposed CVAE-dual-encoder and CVAE-dual-decoder yield results that are in good agreement with the asymptotically unbiased posterior distributions sampled by Markov Chain Monte Carlo (MCMC). The CVAE-vanilla can also be used for estimating posterior distributions, although it has an inferior performance to both CVAE-dual-encoder and CVAE-dual-decoder. CONCLUSIONS: We have evaluated the performance of our deep learning approaches for estimating posterior distributions in dynamic brain PET. Our deep learning approaches yield posterior distributions, which are in good agreement with unbiased distributions estimated by MCMC. All these neural networks have different characteristics and can be chosen by the user for specific applications. The proposed methods are general and can be adapted to other problems. Cornell University 2023-03-17 /pmc/articles/PMC10055492/ /pubmed/36994161 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. |
spellingShingle | Article Liu, Xiaofeng Marin, Thibault Amal, Tiss Woo, Jonghye El Fakhri, Georges Ouyang, Jinsong Posterior Estimation Using Deep Learning: A Simulation Study of Compartmental Modeling in Dynamic PET |
title | Posterior Estimation Using Deep Learning: A Simulation Study of Compartmental Modeling in Dynamic PET |
title_full | Posterior Estimation Using Deep Learning: A Simulation Study of Compartmental Modeling in Dynamic PET |
title_fullStr | Posterior Estimation Using Deep Learning: A Simulation Study of Compartmental Modeling in Dynamic PET |
title_full_unstemmed | Posterior Estimation Using Deep Learning: A Simulation Study of Compartmental Modeling in Dynamic PET |
title_short | Posterior Estimation Using Deep Learning: A Simulation Study of Compartmental Modeling in Dynamic PET |
title_sort | posterior estimation using deep learning: a simulation study of compartmental modeling in dynamic pet |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055492/ https://www.ncbi.nlm.nih.gov/pubmed/36994161 |
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