ArticleEJNMMI research2020
Independent attenuation correction of whole body [
Article in EJNMMI research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy.Frontiers in radiology · 2024Pooled it
- Article
- CT-to-MRI translation of medical volume data based on an enhanced diffusion model.Scientific reports · 2026Article
- Enhanced direct joint attenuation and scatter correction of whole-body PET images via context-aware deep networks.Zeitschrift fur medizinische Physik · 2025Article
- Eliminating the second CT scan of dual-tracer total-body PET/CT via deep learning-based image synthesis and registration.European journal of nuclear medicine and molecular imaging · 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 organ-wise dosimetry ofScientific reports · 2025Article
- CT-Free Attenuation Correction in Paediatric Long Axial Field-of-View Positron Emission Tomography Using Synthetic CT from Emission Data.Diagnostics (Basel, Switzerland) · 2024Article
- Learning CT-free attenuation-corrected total-body PET images through deep learning.European radiology · 2024Article
- Artificial intelligence-based joint attenuation and scatter correction strategies for multi-tracer total-body PET.EJNMMI 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
- A Review of Machine Learning Approaches for Brain Positron Emission Tomography Data Analysis.Nuclear medicine and molecular imaging · 2024Review
- Development of an automated region-of-interest-setting method based on a deep neural network for brain perfusion single photon emission computed tomography quantification methods.Asia Oceania journal of nuclear medicine & biology · 2024Article
- Attenuation Correction of Long Axial Field-of-View Positron Emission Tomography Using Synthetic Computed Tomography Derived from the Emission Data: Application to Low-Count Studies and Multiple Tracers.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial Intelligence-Driven Single-Shot PET Image Artifact Detection and Disentanglement: Toward Routine Clinical Image Quality Assurance.Clinical nuclear medicine · 2023Article
- A review of PET attenuation correction methods for PET-MR.EJNMMI physics · 2023Review
- Synthetic Attenuation Correction Maps for SPECT Imaging Using Deep Learning: A Study on Myocardial Perfusion Imaging.Diagnostics (Basel, Switzerland) · 2023Article
- Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning.European journal of nuclear medicine and molecular imaging · 2023Article
- Quantitative evaluation of a deep learning-based framework to generate whole-body attenuation maps using LSO background radiation in long axial FOV PET scanners.European journal of nuclear medicine and molecular imaging · 2022Article
- Applications of Generative Adversarial Networks (GANs) in Positron Emission Tomography (PET) imaging: A review.European journal of nuclear medicine and molecular imaging · 2022Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAttenuation correction (AC) of PET data is usually performed using a second imaging for the generation of attenuation maps. In certain situations however-when CT- or MR-derived attenuation maps are corrupted or CT acquisition solely for the purpose of AC shall be avoided-it would be of value to have the possibility of obtaining attenuation maps only based on PET information. The purpose of this study was to thus develop, implement, and evaluate a deep learning-based method for whole body [
methodsThe proposed method is investigated on whole body [
resultsQualitative analysis revealed no major SUV deviations on PET
conclusionIndependent AC of whole body [
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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.