ArticleIEEE transactions on image processing : a publication of the IEEE Signal Processing Society2016
Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image Estimation.
Article in IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 62 citations in OpenAlex.
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- Scan-wise generalized PET denoising with contrastive adversarial learning.Physics in medicine and biology · 2026Article
- Diffused Multi-scale Generative Adversarial Network for low-dose PET images reconstruction.Biomedical engineering online · 2025Article
- Deep generative denoising networks enhance quality and accuracy of gated cardiac PET data.Annals of nuclear medicine · 2024Article
- Full-dose whole-body PET synthesis from low-dose PET using high-efficiency denoising diffusion probabilistic model: PET consistency model.Medical physics · 2024Article
- Context-Aware Transformer GAN for Direct Generation of Attenuation and Scatter Corrected PET Data.IEEE transactions on radiation and plasma medical sciences · 2024Article
- Real-time semantic segmentation and anomaly detection of functional images for cell therapy manufacturing.Cytotherapy · 2023Article
- Deep Learning-Based Multiclass Brain Tissue Segmentation in Fetal MRIs.Sensors (Basel, Switzerland) · 2023Article
- 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
- A review on advances inInsights into imaging · 2022Review
- The value of AI in the Diagnosis, Treatment, and Prognosis of Malignant Lung Cancer.Frontiers in radiology · 2022Review
- Applications of artificial intelligence in nuclear medicine image generation.Quantitative imaging in medicine and surgery · 2021Review
- Micro-Networks for Robust MR-Guided Low Count PET Imaging.IEEE transactions on radiation and plasma medical sciences · 2021Article
- Low-Dose 68 Ga-PSMA Prostate PET/MRI Imaging Using Deep Learning Based on MRI Priors.Frontiers in oncology · 2021Article
- A review on medical imaging synthesis using deep learning and its clinical applications.Journal of applied clinical medical physics · 2021Review
- Review and Prospect: Artificial Intelligence in Advanced Medical Imaging.Frontiers in radiology · 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
- 3D Tensor Based Nonlocal Low Rank Approximation in Dynamic PET Reconstruction.Sensors (Basel, Switzerland) · 2019Article
- 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
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 3 countries.
Funding
Abstract
Positron emission tomography (PET) images are widely used in many clinical applications, such as tumor detection and brain disorder diagnosis. To obtain PET images of diagnostic quality, a sufficient amount of radioactive tracer has to be injected into a living body, which will inevitably increase the risk of radiation exposure. On the other hand, if the tracer dose is considerably reduced, the quality of the resulting images would be significantly degraded. It is of great interest to estimate a standard-dose PET (S-PET) image from a low-dose one in order to reduce the risk of radiation exposure and preserve image quality. This may be achieved through mapping both S-PET and low-dose PET data into a common space and then performing patch-based sparse representation. However, a one-size-fits-all common space built from all training patches is unlikely to be optimal for each target S-PET patch, which limits the estimation accuracy. In this paper, we propose a data-driven multi-level canonical correlation analysis scheme to solve this problem. In particular, a subset of training data that is most useful in estimating a target S-PET patch is identified in each level, and then used in the next level to update common space and improve estimation. In addition, we also use multi-modal magnetic resonance images to help improve the estimation with complementary information. Validations on phantom and real human brain data sets show that our method effectively estimates S-PET images and well preserves critical clinical quantification measures, such as standard uptake value.
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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.