Three-dimensional image metric maps for characterizing local image changes associated with AI-based motion correction in coronary CT.

To investigate whether three-dimensional image metric maps can describe the extent and characteristics of local image changes associated with AI-based motion correction using CLEAR Motion in coronary CT. This retrospective single-center study included 24 coronary CT cases reconstructed from the same raw data with and without CLEAR Motion. Three-dimensional maps of structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and deformation vector field (DVF) magnitude were generated after resampling to 0.5-mm isotropic voxels and intensity normalization. Without a true motion-free reference, the anatomical correctness of motion correction could not be directly verified. Therefore, the maps were assessed using spatial congruence analysis with Precision and Recall, patch-wise Spearman correlation analysis with bootstrap confidence intervals, and visual assessment by two readers using a 5-point scale. The three-dimensional image metric maps depicted local image changes predominantly near the coronary arteries, in a distribution consistent with the intended design of CLEAR Motion. Under the main analysis condition, Precision was 90.1% for SSIM, 90.5% for PSNR, 84.6% for DVF magnitude, and 89.2% for absolute difference. Recall values were low, indicating localized rather than diffuse changes. SSIM and PSNR showed a strong positive correlation, whereas both showed negative correlations with DVF magnitude and absolute difference. Visual assessment supported the spatial localization shown by the numerical analysis. Three-dimensional image metric maps based on SSIM, PSNR, and DVF magnitude may be useful for characterizing local image changes associated with AI-based motion correction in coronary CT.