ArticleMagnetic resonance in medicine2021
Deep learning-based T1-enhanced selection of linear attenuation coefficients (DL-TESLA) for PET/MR attenuation correction in dementia neuroimaging.
Article in Magnetic resonance in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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14 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy.Frontiers in radiology · 2024Pooled it
- Denoising of 4D dynamic PET images using spatiotemporal regularization with integrated temporal restoration (SPRINTER).EJNMMI physics · 2026Article
- DREAMER: Rapid and Simultaneous Multiple Contrast Magnetic Resonance Imaging of Solid and Soft Tissue.Magnetic resonance in medicine · 2026Article
- Deep learning prediction of chemo-immunotherapy response using tumor perfusion ultrasound images.Frontiers in artificial intelligence · 2026Article
- Accuracy and Longitudinal Consistency of PET/MR Attenuation Correction in Amyloid PET Imaging amid Software and Hardware Upgrades.AJNR. American journal of neuroradiology · 2025Article
- Three contrasts in 3 min: Rapid, high-resolution, and bone-selective UTE MRI for craniofacial imaging with automated deep-learning skull segmentation.Magnetic resonance in medicine · 2025Article
- MR Cranial Bone Imaging: Evaluation of Both Motion-Corrected and Automated Deep Learning Pseudo-CT Estimated MR Images.AJNR. American journal of neuroradiology · 2024Article
- Current Trends and Applications of PET/MRI Hybrid Imaging in Neurodegenerative Diseases and Normal Aging.Diagnostics (Basel, Switzerland) · 2024Review
- An automatic pipeline for PET/MRI attenuation correction validation in the brain.EJNMMI physics · 2023Article
- A review of PET attenuation correction methods for PET-MR.EJNMMI physics · 2023Review
- Synthetic PET via Domain Translation of 3-D MRI.IEEE transactions on radiation and plasma medical sciences · 2023Article
- Deep-learning synthesized pseudo-CT for MR high-resolution pediatric cranial bone imaging (MR-HiPCB).Magnetic resonance in medicine · 2022Article
- Attenuation Coefficient Estimation for PET/MRI With Bayesian Deep Learning Pseudo-CT and Maximum-Likelihood Estimation of Activity and Attenuation.IEEE transactions on radiation and plasma medical sciences · 2022Article
- EANM procedure guidelines for brain PET imaging using [European journal of nuclear medicine and molecular imaging · 2022Article
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8 authors at 3 institutions in 1 country.
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Abstract
purposeThe accuracy of existing PET/MR attenuation correction (AC) has been limited by a lack of correlation between MR signal and tissue electron density. Based on our finding that longitudinal relaxation rate, or R
methodsDL-TESLA uses a 3D residual UNet (ResUNet) for pseudo-CT (pCT) estimation. With a total of 174 participants, we compared PET AC accuracy of DL-TESLA to 3 other methods adopting similar 3D ResUNet structures but using UTE
resultsDL-TESLA had (1) significantly lower mean absolute error in pCT, (2) the highest Dice coefficients in both bone and air, (3) significantly lower PET relative absolute error in whole brain and various brain regions, (4) the highest percentage of voxels with a PET relative error within both ±3% and ±5%, (5) similar to CT test-retest differences in SUVRs from the cerebrum and mean cortical (MC) region, and (6) similar to CT within-subject coefficient of variation in cerebrum and MC.
conclusionDL-TESLA demonstrates excellent PET/MR AC accuracy and test-retest repeatability.
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