ArticleIEEE transactions on medical imaging2019
3D Auto-Context-Based Locality Adaptive Multi-Modality GANs for PET Synthesis.
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 40 papers, 2 of them syntheses that pooled it.
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Who cites it
40 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep learning for synthetic PET imaging: a systematic mapping review of techniques, metrics, and clinical relevance.European radiology experimental · 2026Pooled it
- Deep learning-based techniques for estimating high-quality full-dose positron emission tomography images from low-dose scans: a systematic review.BMC medical imaging · 2024Pooled 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
- 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
- A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches.ArXiv · 2024Article
- DeepCGAN: early Alzheimer's detection with deep convolutional generative adversarial networks.Frontiers in medicine · 2024Article
- Dose reduction in dynamic synaptic vesicle glycoprotein 2A PET imaging using artificial neural networks.Physics in medicine and biology · 2023Article
- Artificial Intelligence and Deep Learning for Advancing PET Image Reconstruction: State-of-the-Art and Future Directions.Nuklearmedizin. Nuclear medicine · 2023Review
- A cycle-consistent adversarial network for brain PET partial volume correction without prior anatomical information.European journal of nuclear medicine and molecular imaging · 2023Article
- Comparative study of the quantitative accuracy of oncological PET imaging based on deep learning methods.Quantitative imaging in medicine and surgery · 2023Article
- Fan beam CT image synthesis from cone beam CT image using nested residual UNet based conditional generative adversarial network.Physical and engineering sciences in medicine · 2023Article
- Volumetric Analysis of Amygdala and Hippocampal Subfields for Infants with Autism.Journal of autism and developmental disorders · 2023Article
- Synthesizing MR Image Contrast Enhancement Using 3D High-Resolution ConvNets.IEEE transactions on bio-medical engineering · 2023Article
- High-quality PET image synthesis from ultra-low-dose PET/MRI using bi-task deep learning.Quantitative imaging in medicine and surgery · 2022Article
- Artificial intelligence guided enhancement of digital PET: scans as fast as CT?European journal of nuclear medicine and molecular imaging · 2022Article
- 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
- FDG-PET to T1 Weighted MRI Translation with 3D Elicit Generative Adversarial Network (E-GAN).Sensors (Basel, Switzerland) · 2022Article
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10 authors.
Funding
Abstract
Positron emission tomography (PET) has been substantially used recently. To minimize the potential health risk caused by the tracer radiation inherent to PET scans, it is of great interest to synthesize the high-quality PET image from the low-dose one to reduce the radiation exposure. In this paper, we propose a 3D auto-context-based locality adaptive multi-modality generative adversarial networks model (LA-GANs) to synthesize the high-quality FDG PET image from the low-dose one with the accompanying MRI images that provide anatomical information. Our work has four contributions. First, different from the traditional methods that treat each image modality as an input channel and apply the same kernel to convolve the whole image, we argue that the contributions of different modalities could vary at different image locations, and therefore a unified kernel for a whole image is not optimal. To address this issue, we propose a locality adaptive strategy for multi-modality fusion. Second, we utilize 1 ×1 ×1 kernel to learn this locality adaptive fusion so that the number of additional parameters incurred by our method is kept minimum. Third, the proposed locality adaptive fusion mechanism is learned jointly with the PET image synthesis in a 3D conditional GANs model, which generates high-quality PET images by employing large-sized image patches and hierarchical features. Fourth, we apply the auto-context strategy to our scheme and propose an auto-context LA-GANs model to further refine the quality of synthesized images. Experimental results show that our method outperforms the traditional multi-modality fusion methods used in deep networks, as well as the state-of-the-art PET estimation approaches.
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