ArticleQuantitative imaging in medicine and surgery2021
LCPR-Net: low-count PET image reconstruction using the domain transform and cycle-consistent generative adversarial networks.
Article in Quantitative imaging in medicine and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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16 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
- Time-resolved PET imaging using a joint dynamic reconstruction and motion estimation framework (DREME-PET).Medical physics · 2026Article
- Depth-Dependent Performance of Residual Networks for Low-Count PET Image Restoration Using a Dedicated 3D-Printed Striatum Phantom.Bioengineering (Basel, Switzerland) · 2026Article
- Article
- A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches.IEEE transactions on radiation and plasma medical sciences · 2024Article
- A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches.ArXiv · 2024Article
- A two-stage deep-learning framework for CT denoising based on a clinically structure-unaligned paired data set.Quantitative imaging in medicine and surgery · 2024Article
- Prior information-guided reconstruction network for positron emission tomography images.Quantitative imaging in medicine and surgery · 2023Article
- Comparative study of the quantitative accuracy of oncological PET imaging based on deep learning methods.Quantitative imaging in medicine and surgery · 2023Article
- Personalization ofEJNMMI physics · 2023Article
- Evaluation of a respiratory motion-corrected image reconstruction algorithm in 2-[Quantitative imaging in medicine and surgery · 2023Article
- Comparison of the performances of machine learning and deep learning in improving the quality of low dose lung cancer PET images.Japanese journal of radiology · 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
- Pix2Pix generative adversarial network for low dose myocardial perfusion SPECT denoising.Quantitative imaging in medicine and surgery · 2022Article
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15 authors.
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Abstract
backgroundReducing the radiation tracer dose and scanning time during positron emission tomography (PET) imaging can reduce the cost of the tracer, reduce motion artifacts, and increase the efficiency of the scanner. However, the reconstructed images to be noisy. It is very important to reconstruct high-quality images with low-count (LC) data. Therefore, we propose a deep learning method called LCPR-Net, which is used for directly reconstructing full-count (FC) PET images from corresponding LC sinogram data.
methodsBased on the framework of a generative adversarial network (GAN), we enforce a cyclic consistency constraint on the least-squares loss to establish a nonlinear end-to-end mapping process from LC sinograms to FC images. In this process, we merge a convolutional neural network (CNN) and a residual network for feature extraction and image reconstruction. In addition, the domain transform (DT) operation sends a priori information to the cycle-consistent GAN (CycleGAN) network, avoiding the need for a large amount of computational resources to learn this transformation.
resultsThe main advantages of this method are as follows. First, the network can use LC sinogram data as input to directly reconstruct an FC PET image. The reconstruction speed is faster than that provided by model-based iterative reconstruction. Second, reconstruction based on the CycleGAN framework improves the quality of the reconstructed image.
conclusionsCompared with other state-of-the-art methods, the quantitative and qualitative evaluation results show that the proposed method is accurate and effective for FC PET image reconstruction.
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