Similarity-Driven Fine-Tuning Methods for Regularization Parameter Optimization in PET Image Reconstruction

We present an adaptive method for fine-tuning hyperparameters in edge-preserving regularization for PET image reconstruction. For edge-preserving regularization, in addition to the smoothing parameter that balances data fidelity and regularization, one or more control parameters are typically incorp...

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Detalles Bibliográficos
Autores principales: Zhu, Wen, Lee, Soo-Jin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10346317/
https://www.ncbi.nlm.nih.gov/pubmed/37447633
http://dx.doi.org/10.3390/s23135783

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10346317/
https://www.ncbi.nlm.nih.gov/pubmed/37447633
http://dx.doi.org/10.3390/s23135783

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