ArticleNeural networks : the official journal of the International Neural Network Society2020
PET image super-resolution using generative adversarial networks.
Article in Neural networks : the official journal of the International Neural Network Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Review of Generative Adversarial Networks (GANs) for Medical Image Classification and Segmentation.Journal of digital imaging · 2022Pooled it
- Artificial Intelligence Across the PET Reconstruction Pipeline: An Update.AJR. American journal of roentgenology · 2026Review
- A Pilot Study: PET/CT Cross-Modal-Based Multi-Head Fusion Attention Generative Adversarial Network (MHFA-GAN) for PET Image Super-Resolution.Journal of imaging informatics in medicine · 2026Article
- MLPET: a localized neural network approach for probabilistic post-reconstruction PET image analysis using informed priors.EJNMMI physics · 2026Article
- Metabolically Faithful 3D PET Restoration via Volumetric Swin Transformers.Neuroinformatics · 2026Article
- Artificial intelligence for radiopharmaceutical and molecular imaging.Acta pharmaceutica Sinica. B · 2025Review
- Relationship between physical parameters and visual analysis for assessment of image quality: a multi-center and multi-vendor phantom study in brain SPECT.Asia Oceania journal of nuclear medicine & biology · 2025Article
- Super-resolution techniques for biomedical applications and challenges.Biomedical engineering letters · 2024Review
- PET image denoising based on denoising diffusion probabilistic model.European journal of nuclear medicine and molecular imaging · 2024Article
- Machine Learning in Neurosurgery: Toward Complex Inputs, Actionable Predictions, and Generalizable Translations.Cureus · 2024Review
- Brain imaging with portable low-field MRI.Nature reviews bioengineering · 2023Article
- Super-Resolution Processing of Synchrotron CT Images for Automated Fibre Break Analysis of Unidirectional Composites.Polymers · 2023Article
- SynthSR: A public AI tool to turn heterogeneous clinical brain scans into high-resolution T1-weighted images for 3D morphometry.Science advances · 2023Article
- Generative AI for brain image computing and brain network computing: a review.Frontiers in neuroscience · 2023Review
- Application of artificial intelligence in nuclear medicine and molecular imaging: a review of current status and future perspectives for clinical translation.European journal of nuclear medicine and molecular imaging · 2022Review
- Applications of Generative Adversarial Networks (GANs) in Positron Emission Tomography (PET) imaging: A review.European journal of nuclear medicine and molecular imaging · 2022Review
- Deep learning-based image reconstruction and post-processing methods in positron emission tomography for low-dose imaging and resolution enhancement.European journal of nuclear medicine and molecular imaging · 2022Review
- Generative Adversarial Networks in Brain Imaging: A Narrative Review.Journal of imaging · 2022Review
- PET image enhancement using artificial intelligence for better characterization of epilepsy lesions.Frontiers in medicine · 2022Article
- Noise2Void: unsupervised denoising of PET images.Physics in medicine and biology · 2021Article
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Authors and funding
4 authors.
Funding
Abstract
The intrinsically low spatial resolution of positron emission tomography (PET) leads to image quality degradation and inaccurate image-based quantitation. Recently developed supervised super-resolution (SR) approaches are of great relevance to PET but require paired low- and high-resolution images for training, which are usually unavailable for clinical datasets. In this paper, we present a self-supervised SR (SSSR) technique for PET based on dual generative adversarial networks (GANs), which precludes the need for paired training data, ensuring wider applicability and adoptability. The SSSR network receives as inputs a low-resolution PET image, a high-resolution anatomical magnetic resonance (MR) image, spatial information (axial and radial coordinates), and a high-dimensional feature set extracted from an auxiliary CNN which is separately-trained in a supervised manner using paired simulation datasets. The network is trained using a loss function which includes two adversarial loss terms, a cycle consistency term, and a total variation penalty on the SR image. We validate the SSSR technique using a clinical neuroimaging dataset. We demonstrate that SSSR is promising in terms of image quality, peak signal-to-noise ratio, structural similarity index, contrast-to-noise ratio, and an additional no-reference metric developed specifically for SR image quality assessment. Comparisons with other SSSR variants suggest that its high performance is largely attributable to simulation guidance.
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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.