ArticleEuropean journal of nuclear medicine and molecular imaging2026
Dynamic frame-by-frame motion correction for
Article in European journal of nuclear medicine and molecular imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01347710 (A Phase 3, Open Label, Multicenter Study for the Assessment of Myocardial Perfusion Using Positron Emission Tomography), which is not on this 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.
A Phase 3, Open Label, Multicenter Study for the Assessment of Myocardial Perfusion Using Positron Emission Tomography (PET) Imaging of Flurpiridaz F18 Injection in Patients With Suspected or Known Coronary Artery Disease (CAD)
Who cites it
1 citing paper in PubMed.
- Data-Driven Motion Correction Algorithm: Validation in [Journal of clinical medicine · 2026Article
Corrections and comments
- Update of
Authors and funding
9 authors.
Funding
Abstract
purposePrecise quantification of myocardial blood flow (MBF) and flow reserve (MFR) in
methodsThe method employs a 3D-ResNet based architecture that takes 3D PET volumes and outputs motion vectors. It was validated using 5-fold cross-validation on data from 32-sites of a Phase-III clinical trial (NCT01347710). Manual corrections from two experienced operators served as ground truth, and data augmentation using simulated vectors enhanced training robustness. The study compared the DL approach to both manual and standard non-AI automatic MC methods, assessing agreement and diagnostic accuracy using minimal segmental stress MBF and MFR.
resultsThe area under the receiver operating characteristic curves (AUC) for significant CAD were comparable between DL-MC stress MBF, manual-MC stress MBF from Operators (AUC = 0.897, 0.892 and 0.889, respectively; p > 0.05), standard non-AI automatic MC (AUC = 0.877; p > 0.05) and significantly higher than No-MC (AUC = 0.835; p < 0.05). Similar findings were observed with MFR. The 95% confidence limits for agreement with the operator were ± 0.49 (mean difference = 0.00) for MFR and ± 0.24 ml/g/min (mean difference = 0.00) for stress MBF.
conclusionDL-MC is significantly faster but diagnostically comparable to manual-MC. The quantitative results obtained with DL-MC for stress MBF and MFR are in excellent agreement with those manually corrected by experienced operators compared to standard non-AI automatic MC in patients undergoing
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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 OpenQuestion graph.