Myocardial Strain Derived from 13N-ammonia Positron Emission Tomography: Detection of Ischemia-Related Wall Motion Abnormality.

Abstract

Background: Due to the limitation of spatial resolution, cardiac nuclear medicine images have not been applied to feature-tracking method to automatic extraction of myocardial contours. We have successfully applied the feature-tracking method to high-resolution cine ¹³N-ammonia positron emission tomography (PET) images to calculate the regional myocardial strains. Here, we investigate the potential of ¹³N-ammonia PET-derived strain to detect ischemia-related wall motion abnormality. Methods: Data of adenosine-stress/rest ¹³N-ammonia PET for 95 coronary artery disease patients was retrospectively analyzed. Using an original algorithm dedicated to ¹³N-ammonia PET, the longitudinal strain (LS) corresponding to the three main coronary artery territories [right coronary artery (RCA), left anterior descending artery (LAD), and left circumflex coronary artery (LCX)] was calculated from semi-automatic endocardial contours extraction on cine ¹³N-ammonia PET images of the left ventricular long-axis. The presence of ischemia in three main territories was determined from rest and stress-perfusion images. Results: In all three coronary territories, LS at stress was significantly smaller at rest in the ischemic region RCA: -19.2±8.0% vs. -22.7±6.1%, LAD: -19.0±6.9% vs. -24.4±6.4%, LCX: -20.5%±7.6% vs. -22.6±6.9%). In contrast, in the non-ischemic region, there was no significant difference between the LS at stress and at rest. Receiver-operating-characteristic analysis revealed that using the optimal cutoff of the LS ratio of stress to rest, ischemia could be diagnosed with area under the curve of 0.82 in the RCA, 0.86 in the LAD, and 0.69 in the LCX. Conclusions: Myocardial strain derived from endocardial feature-tracking of ¹³N-ammonia PET cine imaging is reduced in the ischemia induced by adenosine-stress. The LS ratio of stress to rest may detect wall motion abnormality related to ischemia.