ArticleIEEE transactions on medical imaging2022
Direct Reconstruction of Linear Parametric Images From Dynamic PET Using Nonlocal Deep Image Prior.
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 20 papers.
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Who cites it
20 citing papers in PubMed.
- Applications of artificial intelligence in nuclear medicine.Zeitschrift fur medizinische Physik · 2026Review
- Deep Image Prior-Incorporated Direct PET Parametric Image Reconstruction.IEEE transactions on medical imaging · 2026Article
- A Cross-modality Transformer Network for MR-guided Low-dose Tau PET Image Denoising.IEEE transactions on radiation and plasma medical sciences · 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
- Spach Transformer: Spatial and Channel-Wise Transformer Based on Local and Global Self-Attentions for PET Image Denoising.IEEE transactions on medical imaging · 2024Article
- A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches.IEEE transactions on radiation and plasma medical sciences · 2024Article
- Deep learning-based PET image denoising and reconstruction: a review.Radiological physics and technology · 2024Review
- ParaPET: non-invasive deep learning method for direct parametric brain PET reconstruction using histoimages.EJNMMI research · 2024Article
- A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches.ArXiv · 2024Article
- Machine learning methods for tracer kinetic modelling.Nuklearmedizin. Nuclear medicine · 2023Review
- Artificial Intelligence and Deep Learning for Advancing PET Image Reconstruction: State-of-the-Art and Future Directions.Nuklearmedizin. Nuclear medicine · 2023Review
- Prior information-guided reconstruction network for positron emission tomography images.Quantitative imaging in medicine and surgery · 2023Article
- Combining deep learning with a kinetic model to predict dynamic PET images and generate parametric images.EJNMMI physics · 2023Article
- Quantitation of dynamic total-body PET imaging: recent developments and future perspectives.European journal of nuclear medicine and molecular imaging · 2023Review
- AI for PET image reconstruction.The British journal of radiology · 2023Review
- A review of PET attenuation correction methods for PET-MR.EJNMMI physics · 2023Review
- Neural KEM: A Kernel Method With Deep Coefficient Prior for PET Image Reconstruction.IEEE transactions on medical imaging · 2023Article
- Artificial Intelligence in Nuclear Medicine: Opportunities, Challenges, and Responsibilities Toward a Trustworthy Ecosystem.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2023Article
- Deep Kernel Representation for Image Reconstruction in PET.IEEE transactions on medical imaging · 2022Article
- Simultaneous PET/MRI: The future gold standard for characterizing motor neuron disease-A clinico-radiological and neuroscientific perspective.Frontiers in neurology · 2022Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Direct reconstruction methods have been developed to estimate parametric images directly from the measured PET sinograms by combining the PET imaging model and tracer kinetics in an integrated framework. Due to limited counts received, signal-to-noise-ratio (SNR) and resolution of parametric images produced by direct reconstruction frameworks are still limited. Recently supervised deep learning methods have been successfully applied to medical imaging denoising/reconstruction when large number of high-quality training labels are available. For static PET imaging, high-quality training labels can be acquired by extending the scanning time. However, this is not feasible for dynamic PET imaging, where the scanning time is already long enough. In this work, we proposed an unsupervised deep learning framework for direct parametric reconstruction from dynamic PET, which was tested on the Patlak model and the relative equilibrium Logan model. The training objective function was based on the PET statistical model. The patient's anatomical prior image, which is readily available from PET/CT or PET/MR scans, was supplied as the network input to provide a manifold constraint, and also utilized to construct a kernel layer to perform non-local feature denoising. The linear kinetic model was embedded in the network structure as a 1 ×1 ×1 convolution layer. Evaluations based on dynamic datasets of
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