ArticleIEEE transactions on medical imaging2019
High Temporal-Resolution Dynamic PET Image Reconstruction Using a New Spatiotemporal Kernel Method.
Article in IEEE transactions on medical imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Cardiovascular imaging with total-body PET.The British journal of radiology · 2026Review
- Coronary artery disease imaging with total-body PET.The British journal of radiology · 2026Review
- Denoising of 4D dynamic PET images using spatiotemporal regularization with integrated temporal restoration (SPRINTER).EJNMMI physics · 2026Article
- Measuring blood-brain barrier dysfunction: a critical appraisal of fluid biomarkers, in vitro models, in vivo imaging, and post-mortem approaches.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Review
- Mapping glucose-induced hemodynamics in white fat depots with label-free optoacoustics.Photoacoustics · 2026Article
- Differences in the quantitative parameter of lung lesions in dynamic chest FDG PET/CT imaging: framing protocol, reconstruction algorithm, software processing.Frontiers in oncology · 2026Article
- 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
- Quantitative PET imaging and modeling of molecular blood-brain barrier permeability.Nature communications · 2025Article
- Quo Vadis HyperpolarizedZeitschrift fur medizinische Physik · 2025Review
- Novel approaches to EEG and MEG in motor neurone disease: IFCN Handbook Chapter.Clinical neurophysiology practice · 2025Review
- Single-scan adaptive graph filtering for dynamic PET denoising by exploring intrinsic spatio-temporal structure.Frontiers in medicine · 2025Article
- Mucociliary clearance is impaired in small airways of cystic fibrosis pigs.American journal of physiology. Lung cellular and molecular physiology · 2024Article
- Optimization-derived blood input function using a kernel method and its evaluation with total-body PET for brain parametric imaging.NeuroImage · 2024Article
- An update on the use of image-derived input functions for human PET studies: new hopes or old illusions?EJNMMI research · 2023Review
- Neural KEM: A Kernel Method With Deep Coefficient Prior for PET Image Reconstruction.IEEE transactions on medical imaging · 2023Article
- HYPR4D kernel method on TOF PET data with validations including image-derived input function.EJNMMI physics · 2022Article
- 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
- Spatiotemporal Kernel Reconstruction for Linear Parametric Neurotransmitter PET Kinetic Modeling in Motion Correction Brain PET of Awake Rats.Frontiers in neuroscience · 2022Article
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Abstract
Current clinical dynamic PET has an effective temporal resolution of 5-10 seconds, which can be adequate for traditional compartmental modeling but is inadequate for exploiting the benefit of more advanced tracer kinetic modeling for characterization of diseases (e.g., cancer and heart disease). There is a need to improve dynamic PET to allow fine temporal sampling of 1-2 seconds. However, the reconstruction of these short-time frames from tomographic data is extremely challenging as the count level of each frame is very low and high noise presents in both spatial and temporal domains. Previously, the kernel framework has been developed and demonstrated as a statistically efficient approach to utilizing image prior for low-count PET image reconstruction. Nevertheless, the existing kernel methods mainly explore spatial correlations in the data and only have a limited ability in suppressing temporal noise. In this paper, we propose a new kernel method which extends the previous spatial kernel method to the general spatiotemporal domain. The new kernelized model encodes both spatial and temporal correlations obtained from image prior information and are incorporated into the PET forward projection model to improve themaximumlikelihood(ML) image reconstruction. Computer simulations and an application to real patient scan have shown that the proposed approach can achieve effective noise reduction in both spatial and temporal domains and outperform the spatial kernel method and conventional ML reconstruction method for improving the high temporal-resolution dynamic PET imaging.
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