ArticleMedical physics2021
Fast dynamic brain PET imaging using stochastic variational prediction for recurrent frame generation.
Article in Medical physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Multimodal Machine Learning Integrating N-13 Ammonia PET and Clinical Variables Predicts Major Adverse Cardiac Events.Journal of imaging informatics in medicine · 2025Article
- A deep learning method for total-body dynamic PET imaging with dual-time-window protocols.European journal of nuclear medicine and molecular imaging · 2025Article
- Bidirectional dynamic frame prediction network for total-body [EJNMMI physics · 2024Article
- A deep learning model for generating [European journal of nuclear medicine and molecular imaging · 2024Article
- A Review of Machine Learning Approaches for Brain Positron Emission Tomography Data Analysis.Nuclear medicine and molecular imaging · 2024Review
- Deep-TOF-PET: Deep learning-guided generation of time-of-flight from non-TOF brain PET images in the image and projection domains.Human brain mapping · 2022Article
- A review on AI in PET imaging.Annals of nuclear medicine · 2022Review
- Fast dynamic brain PET imaging using stochastic variational prediction for recurrent frame generation.Medical physics · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
purposeWe assess the performance of a recurrent frame generation algorithm for prediction of late frames from initial frames in dynamic brain PET imaging.
methodsClinical dynamic
resultsThe predicted dynamic images demonstrated that the model is capable of predicting the trend of change in time-varying tracer biodistribution. The Bland-Altman plots reported the lowest tracer uptake bias (-0.04) for the putamen region and the smallest variance (95% CI: -0.38, +0.14) for the cerebellum. The region-wise Patlak graphical analysis in the caudate and putamen regions for eight subjects from the test and validation dataset showed that the average bias for
conclusionWe have developed a novel deep learning approach for fast dynamic brain PET imaging capable of generating the last 65 minutes time frames from the initial 25 minutes frames, thus enabling significant reduction in scanning time.
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Registered trials
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