ArticleIEEE transactions on medical imaging2022
Deep Kernel Representation for Image Reconstruction in PET.
Article in IEEE transactions on medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Monte Carlo-based scatter correction for the plastic-based modular PET scanner J-PET.Physics in medicine and biology · 2026Article
- Impact of deep learning denoising on kinetic modelling for low-dose dynamic PET: application to single- and dual-tracer imaging protocols.European journal of nuclear medicine and molecular imaging · 2025Article
- Quantitative Total-Body Imaging of Blood Flow with High-Temporal-Resolution Early DynamicJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2025Article
- Total-Body Parametric Imaging Using Relative Patlak Plot.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2025Article
- Quantitative PET imaging and modeling of molecular blood-brain barrier permeability.Nature communications · 2025Article
- Systematic Review on Learning-based Spectral CT.ArXiv · 2024Article
- Deep learning-based PET image denoising and reconstruction: a review.Radiological physics and technology · 2024Review
- Systematic Review on Learning-based Spectral CT.IEEE transactions on radiation and plasma medical sciences · 2024Article
- AI for PET image reconstruction.The British journal of radiology · 2023Review
- Neural KEM: A Kernel Method With Deep Coefficient Prior for PET Image Reconstruction.IEEE transactions on medical imaging · 2023Article
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2 authors.
Funding
Abstract
Image reconstruction for positron emission tomography (PET) is challenging because of the ill-conditioned tomographic problem and low counting statistics. Kernel methods address this challenge by using kernel representation to incorporate image prior information in the forward model of iterative PET image reconstruction. Existing kernel methods construct the kernels commonly using an empirical process, which may lead to unsatisfactory performance. In this paper, we describe the equivalence between the kernel representation and a trainable neural network model. A deep kernel method is then proposed by exploiting a deep neural network to enable automated learning of an improved kernel model and is directly applicable to single subjects in dynamic PET. The training process utilizes available image prior data to form a set of robust kernels in an optimized way rather than empirically. The results from computer simulations and a real patient dataset demonstrate that the proposed deep kernel method can outperform the existing kernel method and neural network method for dynamic PET image reconstruction.
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