<p>Synthetic mammography (SM) derived from digital breast tomosynthesis differs fundamentally from conventional digital mammography (DM) in noise characteristics; however, these differences remain poorly understood. This study aimed to comprehensively characterize the noise structures unique to SM and to clarify their physical origins through comparison with DM. SM and DM images were analyzed using a multi-perspective framework that integrated the normalized noise power spectrum (NPS), noise factor analysis based on the relative standard deviation method, subtraction-based analysis, and pixel-wise signal-to-noise ratio (SNR) maps. Directional NPS was evaluated to assess anisotropy, while subtracted images were used to distinguish fixed-pattern noise from processing-related noise. Noise factor analysis was applied to quantify Poisson, multiplicative, and additive noise contributions, and SNR maps derived from repeated acquisitions were used to evaluate pixel-level reproducibility. Compared with DM, SM images exhibited pronounced anisotropy and stronger spatial correlation in the NPS, reflecting structural noise introduced during image synthesis. The NPS of subtracted images closely matched that of the original SM images, indicating that the dominant noise components were not spatially fixed patterns but rather randomly generated structural texture noise. Noise factor analysis demonstrated that multiplicative noise dominated SM images across all dose levels. No clear differences were observed in the SNR maps, whereas the non-stationary nature of SM noise was confirmed. These results demonstrate that SM noise is dominated by randomly generated structural textures with strong spatial correlations. The findings further emphasize the need for multi-perspective and task-based approaches for accurate assessment and effective clinical utilization of SM images.</p>