Iterative Method Improves Dynamic PET Reconstruction
Published:Dec 30, 2025 16:21
•1 min read
•ArXiv
Analysis
This paper introduces an iterative method (itePGDK) for dynamic PET kernel reconstruction, aiming to reduce noise and improve image quality, particularly in short-duration frames. The method leverages projected gradient descent (PGDK) to calculate the kernel matrix, offering computational efficiency compared to previous deep learning approaches (DeepKernel). The key contribution is the iterative refinement of both the kernel matrix and the reference image using noisy PET data, eliminating the need for high-quality priors. The results demonstrate that itePGDK outperforms DeepKernel and PGDK in terms of bias-variance tradeoff, mean squared error, and parametric map standard error, leading to improved image quality and reduced artifacts, especially in fast-kinetics organs.
Key Takeaways
- •itePGDK is an iterative method for dynamic PET kernel reconstruction.
- •It uses projected gradient descent (PGDK) for kernel matrix calculation.
- •itePGDK eliminates the need for high-quality priors.
- •itePGDK outperforms DeepKernel and PGDK in several metrics.
- •itePGDK improves image quality, especially in short duration frames.
Reference
“itePGDK outperformed these methods in these metrics. Particularly in short duration frames, itePGDK presents less bias and less artifacts in fast kinetics organs uptake compared with DeepKernel.”