ArticleIEEE transactions on bio-medical engineering2017
Semisupervised Tripled Dictionary Learning for Standard-Dose PET Image Prediction Using Low-Dose PET and Multimodal MRI.
Article in IEEE transactions on bio-medical engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Dual-branch residual encoder-decoder convolutional neural network (DB-REDCNN): a computed tomography-integrated multimodal network for positron emission tomography denoising.Quantitative imaging in medicine and surgery · 2025Article
- Robust whole-body PET image denoising using 3D diffusion models: evaluation across various scanners, tracers, and dose levels.European journal of nuclear medicine and molecular imaging · 2025Article
- Diffused Multi-scale Generative Adversarial Network for low-dose PET images reconstruction.Biomedical engineering online · 2025Article
- Realization of high-end PET devices that assist conventional PET devices in improving image quality via diffusion modeling.EJNMMI physics · 2024Article
- IE-CycleGAN: improved cycle consistent adversarial network for unpaired PET image enhancement.European journal of nuclear medicine and molecular imaging · 2024Article
- PET image denoising based on denoising diffusion probabilistic model.European journal of nuclear medicine and molecular imaging · 2024Article
- SMART-PET: a Self-SiMilARiTy-aware generative adversarial framework for reconstructing low-count [18F]-FDG-PET brain imaging.Frontiers in nuclear medicine · 2024Article
- Exploring the Possibility of Measuring Vertebrae Bone Structure Metrics Using MDCT Images: An Unpaired Image-to-Image Translation Method.Bioengineering (Basel, Switzerland) · 2023Article
- 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
- Clinical and phantom validation of a deep learning based denoising algorithm for F-18-FDG PET images from lower detection counting in comparison with the standard acquisition.EJNMMI physics · 2022Article
- A cross-scanner and cross-tracer deep learning method for the recovery of standard-dose imaging quality from low-dose PET.European journal of nuclear medicine and molecular imaging · 2022Article
- 3D Automatic Segmentation of Brain Tumor Based on Deep Neural Network and Multimodal MRI Images.Emergency medicine international · 2022Article
- Applications of artificial intelligence in nuclear medicine image generation.Quantitative imaging in medicine and surgery · 2021Review
- A review of the application of machine learning in molecular imaging.Annals of translational medicine · 2021Review
- Machine learning in quantitative PET: A review of attenuation correction and low-count image reconstruction methods.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2020Review
- Machine Learning in PET: from Photon Detection to Quantitative Image Reconstruction.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2020Article
- Whole-body PET estimation from low count statistics using cycle-consistent generative adversarial networks.Physics in medicine and biology · 2019Article
- 3D Auto-Context-Based Locality Adaptive Multi-Modality GANs for PET Synthesis.IEEE transactions on medical imaging · 2019Article
- Higher SNR PET image prediction using a deep learning model and MRI image.Physics in medicine and biology · 2019Article
- Patch-Based Label Fusion with Structured Discriminant Embedding for Hippocampus Segmentation.Neuroinformatics · 2018Article
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Authors and funding
10 authors.
Funding
Abstract
objectiveTo obtain high-quality positron emission tomography (PET) image with low-dose tracer injection, this study attempts to predict the standard-dose PET (S-PET) image from both its low-dose PET (L-PET) counterpart and corresponding magnetic resonance imaging (MRI).
methodsIt was achieved by patch-based sparse representation (SR), using the training samples with a complete set of MRI, L-PET and S-PET modalities for dictionary construction. However, the number of training samples with complete modalities is often limited. In practice, many samples generally have incomplete modalities (i.e., with one or two missing modalities) that thus cannot be used in the prediction process. In light of this, we develop a semisupervised tripled dictionary learning (SSTDL) method for S-PET image prediction, which can utilize not only the samples with complete modalities (called complete samples) but also the samples with incomplete modalities (called incomplete samples), to take advantage of the large number of available training samples and thus further improve the prediction performance.
resultsValidation was done on a real human brain dataset consisting of 18 subjects, and the results show that our method is superior to the SR and other baseline methods.
conclusionThis paper proposed a new S-PET prediction method, which can significantly improve the PET image quality with low-dose injection. SIGNIFICANCE: The proposed method is favorable in clinical application since it can decrease the potential radiation risk for patients.
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