ArticleEuropean journal of nuclear medicine and molecular imaging2021
Attenuation correction using deep Learning and integrated UTE/multi-echo Dixon sequence: evaluation in amyloid and tau PET imaging.
Article in European journal of nuclear medicine and molecular imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 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
- A proof-of-concept study of multitask learning for cranial synthetic CT generation across heterogeneous MRI field strengths.Medical physics · 2026Article
- Evaluation of regularization factor and attenuation correction method of Q.Clear reconstruction for brain PET/MR in epileptic patients.EJNMMI physics · 2026Article
- A unified deep learning framework for cross-platform harmonization of multi-tracer PET quantification in neurodegenerative disease.NPJ digital medicine · 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
- Current Trends and Applications of PET/MRI Hybrid Imaging in Neurodegenerative Diseases and Normal Aging.Diagnostics (Basel, Switzerland) · 2024Review
- A review of PET attenuation correction methods for PET-MR.EJNMMI physics · 2023Review
- Generation of Digital Brain Phantom for Machine Learning Application of Dopamine Transporter Radionuclide Imaging.Diagnostics (Basel, Switzerland) · 2022Article
- Application of artificial intelligence in brain molecular imaging.Annals of nuclear medicine · 2022Review
- Novel Tracers and Radionuclides in PET Imaging.Radiologic clinics of North America · 2021Review
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
purposePET measures of amyloid and tau pathologies are powerful biomarkers for the diagnosis and monitoring of Alzheimer's disease (AD). Because cortical regions are close to bone, quantitation accuracy of amyloid and tau PET imaging can be significantly influenced by errors of attenuation correction (AC). This work presents an MR-based AC method that combines deep learning with a novel ultrashort time-to-echo (UTE)/multi-echo Dixon (mUTE) sequence for amyloid and tau imaging.
methodsThirty-five subjects that underwent both 11C-PiB and 18F-MK6240 scans were included in this study. The proposed method was compared with Dixon-based atlas method as well as magnetization-prepared rapid acquisition with gradient echo (MPRAGE)- or Dixon-based deep learning methods. The Dice coefficient and validation loss of the generated pseudo-CT images were used for comparison. PET error images regarding standardized uptake value ratio (SUVR) were quantified through regional and surface analysis to evaluate the final AC accuracy.
resultsThe Dice coefficients of the deep learning methods based on MPRAGE, Dixon, and mUTE images were 0.84 (0.91), 0.84 (0.92), and 0.87 (0.94) for the whole-brain (above-eye) bone regions, respectively, higher than the atlas method of 0.52 (0.64). The regional SUVR error for the atlas method was around 6%, higher than the regional SUV error. The regional SUV and SUVR errors for all deep learning methods were below 2%, with mUTE-based deep learning method performing the best. As for the surface analysis, the atlas method showed the largest error (> 10%) near vertices inside superior frontal, lateral occipital, superior parietal, and inferior temporal cortices. The mUTE-based deep learning method resulted in the least number of regions with error higher than 1%, with the largest error (> 5%) showing up near the inferior temporal and medial orbitofrontal cortices.
conclusionDeep learning with mUTE can generate accurate AC for amyloid and tau imaging in PET/MR.
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