ArticleEuropean radiology2019
Direct attenuation correction of brain PET images using only emission data via a deep convolutional encoder-decoder (Deep-DAC).
Article in European radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed, 102 citations in OpenAlex.
- Beyond CT: Attenuation correction for stand-alone brain PET.Zeitschrift fur medizinische Physik · 2026Review
- Generalizable CT-free PET attenuation and scatter correction via few-shot cross domain adaptation.NPJ digital medicine · 2026Article
- Article
- Multi-site distributed training with data protections for PET-based synthetic CT.EJNMMI physics · 2026Article
- Clinical evaluation of deep learning-based CT-free PET reconstruction image: a dual-center study.European journal of nuclear medicine and molecular imaging · 2026Article
- On Hallucinations in Artificial Intelligence-Generated Content for Nuclear Medicine Imaging (the DREAM Report).Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2026Review
- Using multimodal PET+MR data as conditional generative adversarial network inputs improves pseudo-CT and attenuation correction estimates for brain PET/MR.American journal of nuclear medicine and molecular imaging · 2026Article
- Comprehensive review of machine learning and deep learning techniques for epileptic seizure detection and prediction based on neuroimaging modalities.Visual computing for industry, biomedicine, and art · 2025Review
- 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
- The Role of Artificial Intelligence in Advancing Theranostics Dosimetry for Cancer Therapy: a Review.Nuclear medicine and molecular imaging · 2025Review
- 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
- Accuracy and Longitudinal Consistency of PET/MR Attenuation Correction in Amyloid PET Imaging amid Software and Hardware Upgrades.AJNR. American journal of neuroradiology · 2025Article
- Deep-learning-based Attenuation Correction forMolecular imaging and radionuclide therapy · 2024Article
- Learning CT-free attenuation-corrected total-body PET images through deep learning.European radiology · 2024Article
- Deep learning for the harmonization of structural MRI scans: a survey.Biomedical engineering online · 2024Review
- 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
- Contrast-enhanced to non-contrast-enhanced image translation to exploit a clinical data warehouse of T1-weighted brain MRI.BMC medical imaging · 2024Article
Corrections and comments
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Authors and funding
8 authors at 7 institutions in 3 countries.
Funding
Abstract
objectiveTo obtain attenuation-corrected PET images directly from non-attenuation-corrected images using a convolutional encoder-decoder network.
methodsBrain PET images from 129 patients were evaluated. The network was designed to map non-attenuation-corrected (NAC) images to pixel-wise continuously valued measured attenuation-corrected (MAC) PET images via an encoder-decoder architecture. Image quality was evaluated using various evaluation metrics. Image quantification was assessed for 19 radiomic features in 83 brain regions as delineated using the Hammersmith atlas (n30r83). Reliability of measurements was determined using pixel-wise relative errors (RE; %) for radiomic feature values in reference MAC PET images.
resultsPeak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM) values were 39.2 ± 3.65 and 0.989 ± 0.006 for the external validation set, respectively. RE (%) of SUV
conclusionsDirect AC of PET images using deep convolutional encoder-decoder networks is a promising technique for brain PET images. The proposed deep learning method shows significant potential for emission-based AC in PET images with applications in PET/MRI and dedicated brain PET scanners. KEY POINTS: • We demonstrate direct emission-based attenuation correction of PET images without using anatomical information. • We performed radiomics analysis of 83 brain regions to show robustness of direct attenuation correction of PET images. • Deep learning methods have significant promise for emission-based attenuation correction in PET images with potential applications in PET/MRI and dedicated brain PET scanners.
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