ArticleIEEE transactions on computational imaging2020
Super-Resolution PET Imaging Using Convolutional Neural Networks.
Article in IEEE transactions on computational imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 76 citations in OpenAlex.
- Time-resolved PET imaging using a joint dynamic reconstruction and motion estimation framework (DREME-PET).Medical physics · 2026Article
- 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
- PET Imaging in Vulvar Cancer: A Literature Review of the Current Evidence and Clinical Applications.Cancers · 2026Review
- CT-to-PET Synthesis in the Head-Neck and Thoracic Region via Conditional 3D Latent Diffusion Modeling.Bioengineering (Basel, Switzerland) · 2026Article
- Privacy-Preserving Latent Diffusion-Based Synthetic Medical Image Generation.IEEE transactions on medical imaging · 2026Article
- AI-enabled multimodal neuroimaging for neurotransmitter mapping in normal aging and age-related disease.Frontiers in aging neuroscience · 2026Review
- Development and validation of 3D super-resolution convolutional neural network forEJNMMI physics · 2025Article
- Detection of Architectural Dysplastic Features from Histopathological Imagery of Oral Mucosa Using Neural Networks.Bioengineering (Basel, Switzerland) · 2025Article
- Research into super-resolution in medical imaging from 2000 to 2023: bibliometric analysis and visualization.Quantitative imaging in medicine and surgery · 2024Article
- Super-resolution techniques for biomedical applications and challenges.Biomedical engineering letters · 2024Review
- Partial volume correction of PET image data using geometric transfer matrices based on uniform B-splines.Physics in medicine and biology · 2024Article
- Impact of brain segmentation methods on regional metabolism quantification inEJNMMI research · 2023Article
- Super-resolution in brain positron emission tomography using a real-time motion capture system.NeuroImage · 2023Article
- State-of-the-art imaging of neuromodulatory subcortical systems in aging and Alzheimer's disease: Challenges and opportunities.Neuroscience and biobehavioral reviews · 2023Review
- SPECT reconstruction with a trained regularizer using CT-side information: Application toIEEE transactions on computational imaging · 2023Article
- Deep-TOF-PET: Deep learning-guided generation of time-of-flight from non-TOF brain PET images in the image and projection domains.Human brain mapping · 2022Article
- 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
- Application of artificial intelligence in brain molecular imaging.Annals of nuclear medicine · 2022Review
- PET image enhancement using artificial intelligence for better characterization of epilepsy lesions.Frontiers in medicine · 2022Article
- Compensating Positron Range Effects of Ga-68 in Preclinical PET Imaging by Using Convolutional Neural Network: A Monte Carlo Simulation Study.Diagnostics (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Positron emission tomography (PET) suffers from severe resolution limitations which reduce its quantitative accuracy. In this paper, we present a super-resolution (SR) imaging technique for PET based on convolutional neural networks (CNNs). To facilitate the resolution recovery process, we incorporate high-resolution (HR) anatomical information based on magnetic resonance (MR) imaging. We introduce the spatial location information of the input image patches as additional CNN inputs to accommodate the spatially-variant nature of the blur kernels in PET. We compared the performance of shallow (3-layer) and very deep (20-layer) CNNs with various combinations of the following inputs: low-resolution (LR) PET, radial locations, axial locations, and HR MR. To validate the CNN architectures, we performed both realistic simulation studies using the BrainWeb digital phantom and clinical studies using neuroimaging datasets. For both simulation and clinical studies, the LR PET images were based on the Siemens HR+ scanner. Two different scenarios were examined in simulation: one where the target HR image is the ground-truth phantom image and another where the target HR image is based on the Siemens HRRT scanner - a high-resolution dedicated brain PET scanner. The latter scenario was also examined using clinical neuroimaging datasets. A number of factors affected relative performance of the different CNN designs examined, including network depth, target image quality, and the resemblance between the target and anatomical images. In general, however, all deep CNNs outperformed classical penalized deconvolution and partial volume correction techniques by large margins both qualitatively (e.g., edge and contrast recovery) and quantitatively (as indicated by three metrics: peak signal-to-noise-ratio, structural similarity index, and contrast-to-noise ratio).
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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.