Bayesian modeling with locally adaptive prior parameters in small animal imaging
Medical images are hampered by noise and relatively low resolution, which create a bottleneck in obtaining accurate and precise measurements of living organisms. Noise suppression and resolution enhancement are two examples of inverse problems. The aim of this study is to develop novel and robust es...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-03-01
|
| Series: | Frontiers in Nuclear Medicine |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnume.2025.1508816/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850235456161054720 |
|---|---|
| author | Muyang Zhang Robert G. Aykroyd Charalampos Tsoumpas Charalampos Tsoumpas |
| author_facet | Muyang Zhang Robert G. Aykroyd Charalampos Tsoumpas Charalampos Tsoumpas |
| author_sort | Muyang Zhang |
| collection | DOAJ |
| description | Medical images are hampered by noise and relatively low resolution, which create a bottleneck in obtaining accurate and precise measurements of living organisms. Noise suppression and resolution enhancement are two examples of inverse problems. The aim of this study is to develop novel and robust estimation approaches rooted in fundamental statistical concepts that could be utilized in solving several inverse problems in image processing and potentially in image reconstruction. In this study, we have implemented Bayesian methods that have been identified to be particularly useful when there is only limited data but a large number of unknowns. Specifically, we implemented a locally adaptive Markov chain Monte Carlo algorithm and analyzed its robustness by varying its parameters and exposing it to different experimental setups. As an application area, we selected radionuclide imaging using a prototype gamma camera. The results using simulated data compare estimates using the proposed method over the current non-locally adaptive approach in terms of edge recovery, uncertainty, and bias. The locally adaptive Markov chain Monte Carlo algorithm is more flexible, which allows better edge recovery while reducing estimation uncertainty and bias. This results in more robust and reliable outputs for medical imaging applications, leading to improved interpretation and quantification. We have shown that the use of locally adaptive smoothing improves estimation accuracy compared to the homogeneous Bayesian model. |
| format | Article |
| id | doaj-art-8e5b24db6d254e2fbfa92e30ade366be |
| institution | OA Journals |
| issn | 2673-8880 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Nuclear Medicine |
| spelling | doaj-art-8e5b24db6d254e2fbfa92e30ade366be2025-08-20T02:02:15ZengFrontiers Media S.A.Frontiers in Nuclear Medicine2673-88802025-03-01510.3389/fnume.2025.15088161508816Bayesian modeling with locally adaptive prior parameters in small animal imagingMuyang Zhang0Robert G. Aykroyd1Charalampos Tsoumpas2Charalampos Tsoumpas3Department of Statistics, School of Mathematics, University of Leeds, Leeds, United KingdomDepartment of Statistics, School of Mathematics, University of Leeds, Leeds, United KingdomDepartment of Statistics, School of Mathematics, University of Leeds, Leeds, United KingdomDepartment of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, NetherlandsMedical images are hampered by noise and relatively low resolution, which create a bottleneck in obtaining accurate and precise measurements of living organisms. Noise suppression and resolution enhancement are two examples of inverse problems. The aim of this study is to develop novel and robust estimation approaches rooted in fundamental statistical concepts that could be utilized in solving several inverse problems in image processing and potentially in image reconstruction. In this study, we have implemented Bayesian methods that have been identified to be particularly useful when there is only limited data but a large number of unknowns. Specifically, we implemented a locally adaptive Markov chain Monte Carlo algorithm and analyzed its robustness by varying its parameters and exposing it to different experimental setups. As an application area, we selected radionuclide imaging using a prototype gamma camera. The results using simulated data compare estimates using the proposed method over the current non-locally adaptive approach in terms of edge recovery, uncertainty, and bias. The locally adaptive Markov chain Monte Carlo algorithm is more flexible, which allows better edge recovery while reducing estimation uncertainty and bias. This results in more robust and reliable outputs for medical imaging applications, leading to improved interpretation and quantification. We have shown that the use of locally adaptive smoothing improves estimation accuracy compared to the homogeneous Bayesian model.https://www.frontiersin.org/articles/10.3389/fnume.2025.1508816/fullBayesian modelinginhomogeneous parameterimage processingMarkov random fieldMarkov chain Monte Carlo |
| spellingShingle | Muyang Zhang Robert G. Aykroyd Charalampos Tsoumpas Charalampos Tsoumpas Bayesian modeling with locally adaptive prior parameters in small animal imaging Frontiers in Nuclear Medicine Bayesian modeling inhomogeneous parameter image processing Markov random field Markov chain Monte Carlo |
| title | Bayesian modeling with locally adaptive prior parameters in small animal imaging |
| title_full | Bayesian modeling with locally adaptive prior parameters in small animal imaging |
| title_fullStr | Bayesian modeling with locally adaptive prior parameters in small animal imaging |
| title_full_unstemmed | Bayesian modeling with locally adaptive prior parameters in small animal imaging |
| title_short | Bayesian modeling with locally adaptive prior parameters in small animal imaging |
| title_sort | bayesian modeling with locally adaptive prior parameters in small animal imaging |
| topic | Bayesian modeling inhomogeneous parameter image processing Markov random field Markov chain Monte Carlo |
| url | https://www.frontiersin.org/articles/10.3389/fnume.2025.1508816/full |
| work_keys_str_mv | AT muyangzhang bayesianmodelingwithlocallyadaptivepriorparametersinsmallanimalimaging AT robertgaykroyd bayesianmodelingwithlocallyadaptivepriorparametersinsmallanimalimaging AT charalampostsoumpas bayesianmodelingwithlocallyadaptivepriorparametersinsmallanimalimaging AT charalampostsoumpas bayesianmodelingwithlocallyadaptivepriorparametersinsmallanimalimaging |