ArticleMedical image analysis2019
DeepPET: A deep encoder-decoder network for directly solving the PET image reconstruction inverse problem.
Article in Medical image analysis, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 91 papers, 1 of them a synthesis that pooled it.
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Who cites it
91 citing papers in PubMed, 1 synthesis or guideline pooled it, 308 citations in OpenAlex.
- Deep learning for synthetic PET imaging: a systematic mapping review of techniques, metrics, and clinical relevance.European radiology experimental · 2026Pooled it
- Artificial Intelligence Across the PET Reconstruction Pipeline: An Update.AJR. American journal of roentgenology · 2026Review
- Two-stage deep learning framework for the restoration of incomplete-ring PET images.Medical & biological engineering & computing · 2026Article
- Time-resolved PET imaging using a joint dynamic reconstruction and motion estimation framework (DREME-PET).Medical physics · 2026Article
- Applications of artificial intelligence in nuclear medicine.Zeitschrift fur medizinische Physik · 2026Review
- Dual-domain transformer-based learned primal-dual reconstruction for PET imaging.Frontiers in nuclear medicine · 2026Article
- Improved quantification in end-to-end deep learning FastPET reconstruction using multi-view histo-images of attenuation correction factors.IEEE transactions on radiation and plasma medical sciences · 2026Article
- The Role of Artificial Intelligence in Theranostics.Journal of nuclear medicine technology · 2025Review
- Dynamic positron emission tomography image reconstruction using spatiotemporal kernel method with deep image prior.Quantitative imaging in medicine and surgery · 2025Article
- Likelihood-Scheduled Score-Based Generative Modeling for Fully 3D PET Image Reconstruction.IEEE transactions on medical imaging · 2025Article
- Innovations in clinical PET image reconstruction: advances in Bayesian penalized likelihood algorithm and deep learning.Annals of nuclear medicine · 2025Review
- Artificial intelligence and its potential integration with the clinical practice of diagnostic imaging medical physicists: a review.Physical and engineering sciences in medicine · 2025Review
- Optimizing Attenuation Correction inDiagnostics (Basel, Switzerland) · 2025Article
- External phantom-based validation of a deep-learning network trained for upscaling of digital low count PET data.EJNMMI physics · 2025Article
- Towards real-time diffuse optical tomography with a handheld scanning probe.Biomedical optics express · 2025Article
- Machine Learning and Deep Learning Applications in Magnetic Particle Imaging.Journal of magnetic resonance imaging : JMRI · 2025Review
- Monte Carlo methods for medical imaging research.Biomedical engineering letters · 2024Review
- Patient-specific deep learning for 3D protoacoustic image reconstruction and dose verification in proton therapy.Medical physics · 2024Article
- The Adaption of Recent New Concepts in Neural Radiance Fields and Their Role for High-Fidelity Volume Reconstruction in Medical Images.Sensors (Basel, Switzerland) · 2024Article
- The effects of back-projection variants in BPF-like TOF PET reconstruction using CNN filtration - Based on simulated and clinical brain data.Medical physics · 2024Article
31 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
The purpose of this research was to implement a deep learning network to overcome two of the major bottlenecks in improved image reconstruction for clinical positron emission tomography (PET). These are the lack of an automated means for the optimization of advanced image reconstruction algorithms, and the computational expense associated with these state-of-the art methods. We thus present a novel end-to-end PET image reconstruction technique, called DeepPET, based on a deep convolutional encoder-decoder network, which takes PET sinogram data as input and directly and quickly outputs high quality, quantitative PET images. Using simulated data derived from a whole-body digital phantom, we randomly sampled the configurable parameters to generate realistic images, which were each augmented to a total of more than 291,000 reference images. Realistic PET acquisitions of these images were simulated, resulting in noisy sinogram data, used for training, validation, and testing the DeepPET network. We demonstrated that DeepPET generates higher quality images compared to conventional techniques, in terms of relative root mean squared error (11%/53% lower than ordered subset expectation maximization (OSEM)/filtered back-projection (FBP), structural similarity index (1%/11% higher than OSEM/FBP), and peak signal-to-noise ratio (1.1/3.8 dB higher than OSEM/FBP). In addition, we show that DeepPET reconstructs images 108 and 3 times faster than OSEM and FBP, respectively. Finally, DeepPET was successfully applied to real clinical data. This study shows that an end-to-end encoder-decoder network can produce high quality PET images at a fraction of the time compared to conventional methods.
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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.