ArticleJournal of nuclear medicine : official publication, Society of Nuclear Medicine2019
Generation of PET Attenuation Map for Whole-Body Time-of-Flight
Article in Journal of nuclear medicine : official publication, Society of Nuclear Medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.
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Who cites it
62 citing papers in PubMed.
- Bibliometric Analysis of Whole-Body MRI from 2015 to 2025 Across Clinical Applications, Quantitative Imaging, and Artificial Intelligence.Journal of imaging · 2026Article
- Clinical applications of MRI-based artificial intelligence in spinal metastases: A systematic review.Journal of bone oncology · 2026Review
- Dose Prediction Deep Learning-Based Model for VMAT of Prostate Cancer Applying Magnetic Resonance Image (MRI) in Versa HD Linear Accelerator.Advanced biomedical research · 2026Article
- An Investigation on Cross-Tracer Generalizability of Deep Learning-based PET Attenuation Correction.IEEE transactions on radiation and plasma medical sciences · 2025Article
- 2.5D Multi-View Averaging Diffusion Model for 3D Medical Image Translation: Application to Low-Count PET Reconstruction With CT-Less Attenuation Correction.IEEE transactions on medical imaging · 2025Article
- Enhanced direct joint attenuation and scatter correction of whole-body PET images via context-aware deep networks.Zeitschrift fur medizinische Physik · 2025Article
- POUR-Net: A Population-Prior-Aided Over-Under-Representation Network for Low-Count PET Attenuation Map Generation.IEEE transactions on medical imaging · 2025Article
- Deep-learning-based attenuation map generation in kidney single photon emission computed tomography.EJNMMI physics · 2024Article
- Deep-learning-based Attenuation Correction forMolecular imaging and radionuclide therapy · 2024Article
- Artificial intelligence-based joint attenuation and scatter correction strategies for multi-tracer total-body PET.EJNMMI physics · 2024Article
- Artificial Intelligence (AI) in Nuclear Medicine: Is a Friend Not Foe.World journal of nuclear medicine · 2024Article
- Artificial Intelligence-Driven Single-Shot PET Image Artifact Detection and Disentanglement: Toward Routine Clinical Image Quality Assurance.Clinical nuclear medicine · 2023Article
- Differential privacy preserved federated transfer learning for multi-institutionalEuropean journal of nuclear medicine and molecular imaging · 2023Article
- A convolutional neural network-based system to estimate the arterial plasma radioactivity curve in 18 F-FDG dynamic brain PET study.Nuclear medicine communications · 2023Article
- Transfer learning-based attenuation correction for static and dynamic cardiac PET using a generative adversarial network.European journal of nuclear medicine and molecular imaging · 2023Article
- Deep-learning-based methods of attenuation correction for SPECT and PET.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Article
- Physics-Guided Deep Scatter Estimation by Weak Supervision for Quantitative SPECT.IEEE transactions on medical imaging · 2023Article
- A review of PET attenuation correction methods for PET-MR.EJNMMI physics · 2023Review
- Voxel-Based Internal Dosimetry forNuclear medicine and molecular imaging · 2023Article
- Automatic Lung Cancer Segmentation in [Nuclear medicine and molecular imaging · 2023Article
2 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We propose a new deep learning-based approach to provide more accurate whole-body PET/MRI attenuation correction than is possible with the Dixon-based 4-segment method. We use activity and attenuation maps estimated using the maximum-likelihood reconstruction of activity and attenuation (MLAA) algorithm as inputs to a convolutional neural network (CNN) to learn a CT-derived attenuation map.
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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.