ArticleNuclear medicine and molecular imaging2019
Amyloid PET Quantification Via End-to-End Training of a Deep Learning.
Article in Nuclear medicine and molecular imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Impact of adjunctive quantitative analysis on visual interpretation of amyloid PET: a multiple tracer, multicentre study.European radiology · 2026Article
- Deep learning-based amyloid PET harmonization to predict cognitive decline in non-demented elderly.Radiology advances · 2024Article
- A Review of Machine Learning Approaches for Brain Positron Emission Tomography Data Analysis.Nuclear medicine and molecular imaging · 2024Review
- AmyloidPETNet: Classification of Amyloid Positivity in Brain PET Imaging Using End-to-End Deep Learning.Radiology · 2024Article
- Deep Learning-Driven Estimation of Centiloid Scales from Amyloid PET Images withBrain sciences · 2024Article
- Generative AI unlocks PET insights: brain amyloid dynamics and quantification.Frontiers in aging neuroscience · 2024Article
- Estimation of brain amyloid accumulation using deep learning in clinical [EJNMMI physics · 2023Article
- Voxel-Based Internal Dosimetry forNuclear medicine and molecular imaging · 2023Article
- Fast and Accurate Amyloid Brain PET Quantification Without MRI Using Deep Neural Networks.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2023Article
- Automatic Lung Cancer Segmentation in [Nuclear medicine and molecular imaging · 2023Article
- DeepAD: A deep learning application for predicting amyloid standardized uptake value ratio through PET for Alzheimer's prognosis.Frontiers in artificial intelligence · 2023Article
- Quantification of amyloid PET for future clinical use: a state-of-the-art review.European journal of nuclear medicine and molecular imaging · 2022Review
- Development of Amyloid PET Analysis Pipeline Using Deep Learning-Based Brain MRI Segmentation-A Comparative Validation Study.Diagnostics (Basel, Switzerland) · 2022Article
- Prediction of post-stroke cognitive impairment using brain FDG PET: deep learning-based approach.European journal of nuclear medicine and molecular imaging · 2022Article
- Boosting the diagnostic power of amyloid-β PET using a data-driven spatially informed classifier for decision support.Alzheimer's research & therapy · 2021Article
- Deep-learning-based cardiac amyloidosis classification from early acquired pet images.The international journal of cardiovascular imaging · 2021Article
- Visual interpretation of [European journal of nuclear medicine and molecular imaging · 2021Article
- Tau Imaging: New Era of Neuroimaging for Alzheimer's Disease.Nuclear medicine and molecular imaging · 2020Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeAlthough quantification of amyloid positron emission tomography (PET) is important for evaluating patients with cognitive impairment, its routine clinical use is hampered by complicated preprocessing steps and required MRI. Here, we suggested a one-step quantification based on deep learning using native-space amyloid PET images of different radiotracers acquired from multiple centers.
methodsAmyloid PET data of the Alzheimer Disease Neuroimaging Initiative (ADNI) were used for this study. A training/validation consists of 850 florbetapir PET images. Three hundred sixty-six florbetapir and 89 florbetaben PET images were used as test sets to evaluate the model. Native-space amyloid PET images were used as inputs, and the outputs were standardized uptake value ratios (SUVRs) calculated by the conventional MR-based method.
resultsThe mean absolute errors (MAEs) of the composite SUVR were 0.040, 0.060, and 0.050 of training/validation and test sets for florbetapir PET and a test set for florbetaben PET, respectively. The agreement of amyloid positivity measured by Cohen's kappa for test sets of florbetapir and florbetaben PET were 0.87 and 0.89, respectively.
conclusionWe suggest a one-step quantification method for amyloid PET via a deep learning model. The model is highly reliable to quantify the amyloid PET regardless of multicenter images and various radiotracers.
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