2023
DOI: 10.3390/s23135783
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Similarity-Driven Fine-Tuning Methods for Regularization Parameter Optimization in PET Image Reconstruction

Abstract: 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 incorporated to adjust the sensitivity of edge preservation by modifying the shape of the penalty function. Although there have been efforts to develop automated methods for tuning the hyperparameters in regularized PET r… Show more

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