ArticleEJNMMI physics2026
The impact of data driven motion correction for clinical brain PET/MRI radiotracers.
Article in EJNMMI physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeThe Positron Emission Tomography (PET)/Magnetic Resonance Imaging (MRI) scanner combines two diagnostic imaging modalities, providing information on anatomy and physiology. Beneficial diagnosis areas are epilepsy ([[Formula: see text]F]Fluorodeoxyglucose (FDG)) and cancer recurrence ([[Formula: see text]C]methionine (MET)), where subject motion during PET acquisition reduces image quality, potentially compromising diagnostic accuracy. This project aimed to evaluate the impact of PET data-driven motion correction (ddMC) of these clinical PET radiotracers and to assess, for the first time, whether the automatic motion categorization reflects motion levels impacting the image quality.
methodsEighty-nine PET scans (66 [[Formula: see text]C]MET, 23 [[Formula: see text]F]FDG) were reconstructed with ddMC and without motion correction (noMC) using the research software lmDuetto toolbox (GE Healthcare, Chicago, IL, USA), and were automatically categorized into motion groups. MRI images were segmented, and the regions of interest (ROIs) transferred to the PET space. The effect of ddMC was analyzed by relative signal differences between ddMC and noMC. Motion estimation and categorization were evaluated by normalized cross correlation (XC) over time and the proposed cumulative displacement-time histogram (cDTH).
resultsOverall, ddMC increased signal values within cortical ROIs compared to noMC. In the high motion category, median relative mean signal differences were 0.61% (0.41-0.80%) for [[Formula: see text]F]FDG and 0.70% (0.61-0.79%) for [[Formula: see text]C]MET. The XC improved ([[Formula: see text]F]FDG: 0.80 to 0.97, [[Formula: see text]C]MET: 0.85 to 0.98). Low and medium motion groups had lesser impact, indicating motion correction is most relevant for high motion. The XC and cDTH identified subjects whose motion classification should be revised.
conclusionIn conclusion, the results confirm previous findings with ddMC using [[Formula: see text]F]FDG and demonstrate its suitability for lower-accumulating [[Formula: see text]C]MET. The automatic motion categorization needs re-evaluation to better reflect motion affecting PET image quality.
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