ArticleNuclear medicine and molecular imaging2024
Accurate Automated Quantification of Dopamine Transporter PET Without MRI Using Deep Learning-based Spatial Normalization.
Article in Nuclear medicine and molecular imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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
5 citing papers in PubMed.
- Equivocal Dopamine Transporter Imaging: Integrating Visual Assessment, Quantification, and Clinical Follow-Up.Life (Basel, Switzerland) · 2026Review
- Feasibility Study of Deep Learning-Driven Image Restoration for Fast, Low-Count [Diagnostics (Basel, Switzerland) · 2026Article
- First-in-Human Imaging Results of PHAROS: A Versatile High-Resolution TOF/DOI PET Scanner for Brain, Breast, and Extremity Imaging.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2026Article
- An interactive cascaded deep learning framework with expert refinement for accurate striatal subregion segmentation.Scientific reports · 2026Article
- Development of AI-based dopamine transporter (DAT) image generation technique using early phase [18F]-FP-CIT PET imaging.PloS one · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Dopamine transporter imaging is crucial for assessing presynaptic dopaminergic neurons in Parkinson's disease (PD) and related parkinsonian disorders. While Methods: The proposed SN engine consists of convolutional neural networks, trained using 213 paired datasets of Results: The proposed AI-based method successfully generated spatially normalized Conclusion: Our AI-based SN method enables accurate automatic quantification of striatal activity in
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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 Socratic graph.