ArticleEJNMMI physics2025
Validation of a data-driven motion-compensated PET brain image reconstruction algorithm in clinical patients using four radiotracers.
Article in EJNMMI physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Time-resolved PET imaging using a joint dynamic reconstruction and motion estimation framework (DREME-PET).Medical physics · 2026Article
- Optimization of brain PET markerless head motion correction in a large human cohort.EJNMMI physics · 2026Article
- The impact of data driven motion correction for clinical brain PET/MRI radiotracers.EJNMMI physics · 2026Article
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13 authors.
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Abstract
purposePatients with dementia symptoms often struggle to limit movements during PET examinations, necessitating motion compensation in brain PET imaging to ensure the high image quality needed for diagnostic accuracy. This study validates a data-driven motion-compensated (MoCo) PET brain image reconstruction algorithm that corrects head motion by integrating the detected motion frames and their associated rigid body transformations into the iterative image reconstruction. Validation was conducted using phantom scans, healthy volunteers, and clinical patients using four radiotracers with distinct tracer activity distributions.
methodsWe conducted technical validation experiments of the algorithm using Hoffman brain phantom scans during a series of controlled movements, followed by two blinded reader studies assessing image quality between standard images and MoCo images in 38 clinical patients receiving dementia scans with [
resultsThe Hoffman brain phantom study demonstrated the algorithm's capability to detect and correct for even minimal movements, 1-mm translations and 1⁰ rotations, applied to the phantom. Within the clinical cohort, where standard images were deemed suboptimal or non-diagnostic, all MoCo images were classified as having acceptable diagnostic quality. In the research cohort, MoCo images consistently matched or surpassed the standard image quality even in cases with minimal head movement, and the MoCo algorithm never led to degraded image quality.
conclusionThe PET brain MoCo reconstruction algorithm was robust and worked well for four different tracers with markedly different uptake patterns. Moco images markedly improved the image quality for patients who were unable to lie still during a PET examination and obviated the need for any repeat scans. Thus, the method was clinically feasible and has the potential for improving diagnostic accuracy.
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