Effects of motion and white matter hyperintensities on reference region based ¹⁸F-SynVesT-1 PET quantification in Alzheimer's disease and healthy volunteers.

@article{carron2026effects,
  title = {{Effects of motion and white matter hyperintensities on reference region based ¹⁸F-SynVesT-1 PET quantification in Alzheimer's disease and healthy volunteers.}},
  author = {Charles Carron and Simon Noë and Greet Vanderlinden and Michel Koole and Georg Schramm and Koen Van Laere},
  journal = {EJNMMI research},
  year = {2026},
  month = aug,
  doi = {10.1186/s13550-026-01496-8},
  url = {https://europepmc.org/article/MED/42957873},
  note = {CC BY-NC-ND},
}

TY  - JOUR
TI  - Effects of motion and white matter hyperintensities on reference region based ¹⁸F-SynVesT-1 PET quantification in Alzheimer's disease and healthy volunteers.
AU  - Charles Carron
AU  - Simon Noë
AU  - Greet Vanderlinden
AU  - Michel Koole
AU  - Georg Schramm
AU  - Koen Van Laere
PY  - 2026
DA  - 2026/08/03
JO  - EJNMMI research
DO  - 10.1186/s13550-026-01496-8
UR  - https://europepmc.org/article/MED/42957873
AB  - Background  ¹⁸F-SynVesT-1 is a PET tracer targeting synaptic vesicle protein 2A, a surrogate marker of synaptic density. Simplified quantification using pseudo-reference regions such as the centrum semiovale (CSO) or whole cerebellum (CER) has previously been validated in healthy volunteers and Alzheimer's disease. This study assessed how motion correction and white-matter hyperintensities (WMH) affect ¹⁸F-SynVesT-1 quantification. We reconstructed ¹⁸F-SynVesT-1 images for 40 healthy volunteers and 31 patients with Alzheimer's disease 60-90 minutes post-injection with 3 motion handling methods: no motion correction; 5 minute frame-based post-reconstruction coregistration and averaging, and a high-temporal-resolution data-driven motion correction applied during reconstruction. WMH were segmented from T2-FLAIR images and excluded from the CSO to generate lesion-corrected CSO regions. A region-based voxelwise partial volume correction was applied. SUV and SUVRCSO/CER were compared across motion- and lesion-handling methods and the effects on group differentiation were explored.Results  Data-driven motion correction during reconstruction improved visual image sharpness and increased SUV in high-binding cortical regions (median +0.3-1.0%, p≤0.001) while reducing SUV in the low-binding CSO (median -0.5%, p=0.001). Coregistering and averaging of frames only increased contrast when there was severe motion. The cerebellum was one of the least affected regions by motion correction (median SUV +0.4% with PETDDLM, p<0.001). WMH affected CSO SUV only when ≥25% of the region contained lesions and never affected the cerebellar SUV. SUVRCSO yielded better group differentiation than SUVRCER after accounting for motion and WMH. Individual patients' z-scores differed significantly in both directions depending on the use of SUVRCSO or SUVRCER (e.g. mean absolute difference of 1.00± 0.82 in the precuneus).Conclusion  Data-driven motion correction during reconstruction reduced the impact of head movements and attenuation mismatches, improving image contrast and quantitative accuracy. SUVRCER was less sensitive to motion and WMH than SUVRCSO, indicating greater robustness. When motion and WMH effects were controlled, SUVRCSO provided slightly better group differentiation than SUVRCER. These findings suggest that CSO may be preferred for group comparisons in well-controlled datasets, while the cerebellum represents a more robust reference region in the presence of motion or substantial WMH burden. The study was registered on ClinicalTrials.gov (NCT05384353), first posted on 20 May 2022.
SN  - 2191-219X
ER  -