ArticleHuman brain mapping2022
Deep-TOF-PET: Deep learning-guided generation of time-of-flight from non-TOF brain PET images in the image and projection domains.
Article in Human brain mapping, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Deep learning-based time-of-flight (ToF) enhancement of non-ToF PET scans for different radiotracers.European journal of nuclear medicine and molecular imaging · 2025Article
- A new design to improve time resolution in a time of flight brain PET using dual layer offset scintillator crystals.Scientific reports · 2025Article
- Effects of a deep learning-based image quality enhancement method on a digital-BGO PET/CT system forEJNMMI physics · 2025Article
- A deep learning model for generating [European journal of nuclear medicine and molecular imaging · 2024Article
- Artificial intelligence-based analysis of whole-body bone scintigraphy: The quest for the optimal deep learning algorithm and comparison with human observer performance.Zeitschrift fur medizinische Physik · 2024Article
- Perspectives of the European Association of Nuclear Medicine on the role of artificial intelligence (AI) in molecular brain imaging.European journal of nuclear medicine and molecular imaging · 2024Article
- The quest for multifunctional and dedicated PET instrumentation with irregular geometries.Annals of nuclear medicine · 2024Review
- Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning.European journal of nuclear medicine and molecular imaging · 2023Article
- Comparative effectiveness of standard vs. AI-assisted PET/CT reading workflow for pre-treatment lymphoma staging: a multi-institutional reader study evaluation.Frontiers in nuclear medicine · 2023Article
- 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We aim to synthesize brain time-of-flight (TOF) PET images/sinograms from their corresponding non-TOF information in the image space (IS) and sinogram space (SS) to increase the signal-to-noise ratio (SNR) and contrast of abnormalities, and decrease the bias in tracer uptake quantification. One hundred forty clinical brain
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.