ArticlePloS one2019
Attenuation correction using 3D deep convolutional neural network for brain 18F-FDG PET/MR: Comparison with Atlas, ZTE and CT based attenuation correction.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 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
- Enhanced direct joint attenuation and scatter correction of whole-body PET images via context-aware deep networks.Zeitschrift fur medizinische Physik · 2025Article
- Learning CT-free attenuation-corrected total-body PET images through deep learning.European radiology · 2024Article
- Current Trends and Applications of PET/MRI Hybrid Imaging in Neurodegenerative Diseases and Normal Aging.Diagnostics (Basel, Switzerland) · 2024Review
- 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
- A review of PET attenuation correction methods for PET-MR.EJNMMI physics · 2023Review
- Deep-learning synthesized pseudo-CT for MR high-resolution pediatric cranial bone imaging (MR-HiPCB).Magnetic resonance in medicine · 2022Article
- Zero-TE MRI: principles and applications in the head and neck.The British journal of radiology · 2022Review
- Deep learning for Dixon MRI-based attenuation correction in PET/MRI of head and neck cancer patients.EJNMMI physics · 2022Article
- MRI-guided attenuation correction in torso PET/MRI: Assessment of segmentation-, atlas-, and deep learning-based approaches in the presence of outliers.Magnetic resonance in medicine · 2022Article
- Application of artificial intelligence in brain molecular imaging.Annals of nuclear medicine · 2022Review
- Artificial Intelligence-Based Data Corrections for Attenuation and Scatter in Position Emission Tomography and Single-Photon Emission Computed Tomography.PET clinics · 2021Review
- Deep learning-based T1-enhanced selection of linear attenuation coefficients (DL-TESLA) for PET/MR attenuation correction in dementia neuroimaging.Magnetic resonance in medicine · 2021Article
- Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians.Journal of magnetic resonance imaging : JMRI · 2021Review
- MR-based Attenuation Correction for Brain PET Using 3D Cycle-Consistent Adversarial Network.IEEE transactions on radiation and plasma medical sciences · 2021Article
- A review on medical imaging synthesis using deep learning and its clinical applications.Journal of applied clinical medical physics · 2021Review
- Accuracy and precision of zero-echo-time, single- and multi-atlas attenuation correction for dynamic [EJNMMI physics · 2020Article
- Attenuation correction for human PET/MRI studies.Physics in medicine and biology · 2020Article
- Improved PET/MRI attenuation correction in the pelvic region using a statistical decomposition method on T2-weighted images.EJNMMI physics · 2020Article
- Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy.European journal of hybrid imaging · 2020Review
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Authors and funding
10 authors.
Funding
Abstract
One of the main technical challenges of PET/MRI is to achieve an accurate PET attenuation correction (AC) estimation. In current systems, AC is accomplished by generating an MRI-based surrogate computed tomography (CT) from which AC-maps are derived. Nevertheless, all techniques currently implemented in clinical routine suffer from bias. We present here a convolutional neural network (CNN) that generated AC-maps from Zero Echo Time (ZTE) MR images. Seventy patients referred to our institution for 18FDG-PET/MR exam (SIGNA PET/MR, GE Healthcare) as part of the investigation of suspected dementia, were included. 23 patients were added to the training set of the manufacturer and 47 were used for validation. Brain computed tomography (CT) scan, two-point LAVA-flex MRI (for atlas-based AC) and ZTE-MRI were available in all patients. Three AC methods were evaluated and compared to CT-based AC (CTAC): one based on a single head-atlas, one based on ZTE-segmentation and one CNN with a 3D U-net architecture to generate AC maps from ZTE MR images. Impact on brain metabolism was evaluated combining voxel and regions-of-interest based analyses with CTAC set as reference. The U-net AC method yielded the lowest bias, the lowest inter-individual and inter-regional variability compared to PET images reconstructed with ZTE and Atlas methods. The impact on brain metabolism was negligible with average errors of -0.2% in most cortical regions. These results suggest that the U-net AC is more reliable for correcting photon attenuation in brain FDG-PET/MR than atlas-AC and ZTE-AC methods.
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