An improved Scale-Invariant Feature Transform-based method for detecting tumor tissue motion induced by respiration

BACKGROUND: Scale-Invariant Feature Transform (SIFT) features are widely used in target recognition and tracking. This study aimed to exploit the robust performance of the SIFT algorithm to accurately calculate tumor tissue displacement during respiratory motion. MATERIALS AND METHODS: A thoracic phantom was employed in this study. Eight synthetic nodules with different shapes and gray values were inserted into the phantom. First, the nodules were displaced upward, downward, leftward, and rightward by 10 pixels to simulate motion in different directions during respiration. The nodules were then rotated clockwise and counterclockwise by 5°, 10°, 30°, and 45°. Subsequently, the SIFT and cross-correlation algorithms were applied to analyze the phantoms. Finally, a t-test was used to assess differences among motion directions. RESULTS: The t-test result indicated no significant difference in the detection of moving phantom across all motion directions (p > 0.05). No statistically significant difference was observed between SIFT and cross-correlation in detecting translational motion. In contrast, the results obtained from rotating phantoms demonstrated that SIFT could effectively detect rotational distortion. CONCLUSION: The SIFT algorithm can be used to calculate tissue distortion caused by thoracic motion during respiration.