Background
Motion of the heart is known to affect image quality in cardiac PET. The prevalence of motion blurring in routine cardiac PET is not fully appreciated due to challenges identifying subtle motion artefacts. This study utilizes a recent prototype Data-Driven Motion Correction (DDMC) algorithm to generate corrected images that are compared with non-corrected images to identify visual differences in relative rubidium-82 perfusion images due to motion.
Methods
300 stress and 300 rest static images were reconstructed with DDMC and without correction (NMC). The 600 DDMC/NMC image pairs were assigned Visual Difference Score (VDS). The number of non-diagnostic images were noted. A “Dwell Fraction” (DF) was derived from the data to quantify motion and predict image degradation.
Results
Motion degradation (VDS = 1 or 2) was evident in 58% of stress images and 33% of rest images. Seven NMC images were non-diagnostic—these originated from six studies giving a 2% rate of non-diagnostic studies due to motion. The DF metric was able to effectively predict image degradation. The DDMC heart identification and tracking was successful in all images.
Conclusion
Motion degradation is present in almost half of all relative perfusion images. The DDMC algorithm is a robust tool for predicting, assessing and correcting image degradation.
