ArticleFrontiers in nuclear medicine2025
MR-guided reconstruction of PET data in spinal cord PET/MRI.
Article in Frontiers in nuclear medicine, 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.
- A Novel Marker-Based Registration Method for Simultaneous Preclinical PET/MR with a Non-Stationary PET Detector.Research square · 2026Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Accurate PET reconstruction in spinal cord PET/MRI is challenging due to the small size of the structure and interference from background activity. The aim of this study was to establish whether MR-guided PET reconstruction can improve the accuracy of measured uptake in the spinal cord. Methods: The hybrid kernel expectation maximisation (HKEM) algorithm was evaluated on a digital anthropomorphic phantom (XNAT), and an implementation of a modified asymmetric Bowsher's prior incorporating both PET and MR data was evaluated on clinical test cases. The methods were compared against commonly used algorithms OSEM and Q.Clear. Results: The results demonstrated that the two algorithms lead to an increase in measured [ Discussion: With care taken to optimise for the desired application, novel PET image reconstruction algorithms using PET and MR data to inform iterative image updates lead to improved quantification and improved image quality compared to OSEM. Further work is needed to investigate the optimal parameters and identify strategies to reduce residual bias.
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