ArticleEuropean journal of nuclear medicine and molecular imaging2025
Automated quantification of brain PET in PET/CT using deep learning-based CT-to-MR translation: a feasibility study.
Article in European journal of nuclear medicine and molecular imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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Who cites it
1 citing paper in PubMed.
- Centiloid values from deep learning-based CT parcellation: a valid alternative to freesurfer.Alzheimer's research & therapy · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
purposeQuantitative analysis of PET images in brain PET/CT relies on MRI-derived regions of interest (ROIs). However, the pairs of PET/CT and MR images are not always available, and their alignment is challenging if their acquisition times differ considerably. To address these problems, this study proposes a deep learning framework for translating CT of PET/CT to synthetic MR images (MR
methodsIn this retrospective study, 139 subjects who underwent brain [
resultsCompared to MR
conclusionWe demonstrated a deep learning framework for automated regional brain analysis in PET/CT with MR
Indexed as
Identifiers
39964542What 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.