QUANTITATIVE EVALUATION OF MOTION CORRECTION ALGORITHMS IN MYOCARDIAL PERFUSION SPECT IMAGING USING CLINICAL AND SIMULATED DATA

Abstract

During myocardial perfusion SPECT imaging, patient motion remains a persistent challenge in SPECT imaging, often leading to image degradation and misinterpretation of perfusion defects. It is obvious that when motions occurred in patient studies, there were alterations in the perfusion defects in the anterior and inferior walls. Various Algorithms are used to correct the motion during imaging. In this study, three motion correction techniques that is Stasis, Hopkins, and Motion Detection and Correction (MDC) were evaluated using both simulated motion patterns and clinical data. Image reconstruction was performed using the ordered subset expectation maximization (OSEM) algorithm. Motion introduced artificial defects, particularly in the anterior and inferior myocardial regions. Among the evaluated techniques, MDC demonstrated superior performance, achieving an artifact reduction of approximately 85-92%, compared to 65-70% for Stasis and 50- 60% for Hopkins. Statistical analysis using one-way ANOVA confirmed that the improvement was significant (p < 0.05). These findings suggest that MDC provides a reliable framework for motion correction and may improve diagnostic confidence in clinical SPECT imaging.