ArticlePhysics in medicine and biology2016
Anatomically-aided PET reconstruction using the kernel method.
Article in Physics in medicine and biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- MLPET: a localized neural network approach for probabilistic post-reconstruction PET image analysis using informed priors.EJNMMI physics · 2026Article
- Critical review of partial volume correction methods in PET and SPECT imaging: benefits, pitfalls, challenges, and future outlook.European journal of nuclear medicine and molecular imaging · 2026Review
- Dynamic positron emission tomography image reconstruction using spatiotemporal kernel method with deep image prior.Quantitative imaging in medicine and surgery · 2025Article
- 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
- MR-guided reconstruction of PET data in spinal cord PET/MRI.Frontiers in nuclear medicine · 2025Article
- Article
- Triple modality image reconstruction of PET data using SPECT, PET, CT information increases lesion uptake in images of patients treated with radioembolization withEJNMMI physics · 2023Article
- Neural KEM: A Kernel Method With Deep Coefficient Prior for PET Image Reconstruction.IEEE transactions on medical imaging · 2023Article
- Investigation and optimization of PET-guided SPECT reconstructions for improved radionuclide therapy dosimetry estimates.Frontiers in nuclear medicine · 2023Article
- SPECT reconstruction with a trained regularizer using CT-side information: Application toIEEE transactions on computational imaging · 2023Article
- Deep Kernel Representation for Image Reconstruction in PET.IEEE transactions on medical imaging · 2022Article
- Hybrid kernelised expectation maximisation for Bremsstrahlung SPECT reconstruction in SIRT withEJNMMI physics · 2022Article
- Direct Reconstruction of Linear Parametric Images From Dynamic PET Using Nonlocal Deep Image Prior.IEEE transactions on medical imaging · 2022Article
- Probabilistic deconvolution of PET images using informed priors.Frontiers in nuclear medicine · 2022Article
- Anatomically aided PET image reconstruction using deep neural networks.Medical physics · 2021Article
- Modified kernel MLAA using autoencoder for PET-enabled dual-energy CT.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2021Article
- Improved identification of abdominal aortic aneurysm using the Kernelized Expectation Maximization algorithm.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2021Article
- Theranostic SPECT reconstruction for improved resolution: application to radionuclide therapy dosimetry.EJNMMI physics · 2021Article
- Neural MLAA for PET-enabled Dual-Energy CT Imaging.Proceedings of SPIE--the International Society for Optical Engineering · 2021Article
- 3D/4D Reconstruction and Quantitative Total Body Imaging.PET clinics · 2021Review
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Authors and funding
5 authors.
Funding
Abstract
This paper extends the kernel method that was proposed previously for dynamic PET reconstruction, to incorporate anatomical side information into the PET reconstruction model. In contrast to existing methods that incorporate anatomical information using a penalized likelihood framework, the proposed method incorporates this information in the simpler maximum likelihood (ML) formulation and is amenable to ordered subsets. The new method also does not require any segmentation of the anatomical image to obtain edge information. We compare the kernel method with the Bowsher method for anatomically-aided PET image reconstruction through a simulated data set. Computer simulations demonstrate that the kernel method offers advantages over the Bowsher method in region of interest quantification. Additionally the kernel method is applied to a 3D patient data set. The kernel method results in reduced noise at a matched contrast level compared with the conventional ML expectation maximization algorithm.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.