Evaluation of motion correction on myocardial [1 8F]F-AraG PET uptake using data-driven respiratory gating
Hannah Kim1, Ryan Tang1, Uttam M Shrestha1
1Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.
Background:
[1 8F]F-AraG (radio-fluorinated 9-β-D-arabinofuranosylguanine) is a PET radiotracer for imaging T-cell activation with emerging applications in myocardial imaging. Cardiac PET is particularly susceptible to respiratory motion, which degrades spatial resolution and introduces regional variability in quantitative measurements.
Purpose:
To evaluate the impact of data-driven respiratory motion correction on myocardial image quality and quantitative stability in [1 8F]F-AraG PET within a practical whole-body imaging workflow.
Methods:
Respiratory motion correction was performed using a vendor-implemented data-driven gating approach (OncoFreeze AI, Siemens Healthineers), which derives motion signals from PET coincidence data without external hardware and applies phase-based binning with motion-compensated reconstruction. [1 8F]F-AraG PET scans from 15 healthy subjects were analyzed using two myocardial segmentation strategies: a threshold-based method and a fixed-size VOI approach. Quantitative metrics (SUVmean, SUVmax, and volume) were assessed alongside spatial resolution using full width at half maximum (FWHM) as a surrogate of motion-induced blurring.
Results:
Motion correction reduced myocardial FWHM on average (global mean decrease ∼0.6 mm), indicating improved spatial resolution and reduced motion-related blurring. Threshold-based segmentation yielded variable uptake and volume estimates due to spillover and saturation effects, whereas the fixed VOI approach demonstrated preserved quantitative uptake with minimal changes in SUVmean (< 1%). Motion correction reduced apparent myocardial volume, consistent with improved myocardial boundary definition. While variability in FWHM and SUV persisted due to redistribution of counts across respiratory bins, the overall effect reflected a balance between reduced motion blur and increased statistical noise.
Conclusions:
Data-driven respiratory motion correction improves myocardial spatial resolution and reduces regional differences in motion-related blurring in [1 8F]F-AraG PET while preserving quantitative uptake measurements. Within a standard whole-body PET workflow without dedicated cardiac gating, this approach provides a practical strategy to enhance image fidelity, particularly for tracers with subtle myocardial uptake.
