Self-calibrated pixel-attention autoencoder: A strategy for contrast-enhanced spectral mammography image-to-image translation.

Background and objective: Contrast-enhanced spectral mammography (CESM) is a new imaging modality that integrates digital mammography and the use of iodinated contrast agents in a dual-energy acquisition protocol, facilitating the differentiation between benign and malignant breast lesions. However, its clinical use is constrained by the potential risks associated with iodinated contrast agents and the increased radiation exposure. Method: The proposed strategy adapts a self-calibrated pixel attention (SCPA) mechanism to enhance the ability of conditional image-to-image translation models to synthesize contrast uptake in breast lesions. To assess the generalizability and feasibility of our approach under realistic data variations, we used two independent CESM datasets. The performance of the model was evaluated through quantitative and qualitative analyses, considering both whole-image performance and contrast-uptake regions. The results were compared with state-of-the-art conditional generative models. Furthermore, the clinical reliability of the images synthesized by the proposed strategy was assessed through a qualitative analysis performed by an expert breast radiologist. Results: The proposed strategy outperformed state-of-the-art approaches in the synthesis of both whole images and contrast-enhanced regions. In particular, the SCPA-conditional generative adversarial network exhibited superior preservation of the morphology of the lesion while maintaining the overall quality of the generated images. Moreover, the clinical feasibility evaluation showed acceptable realism and technical quality suitable for radiologist interpretation, although in some cases, residual synthesis errors were observed that could affect patient management. Conclusion: The integration of attention mechanisms substantially improves the synthesis of contrast uptake in regions of interest (ROIs) within CESM studies with limited and heterogeneous datasets. These findings demonstrate the feasibility of generating contrast enhancement from non-contrast mammography images, highlighting the potential of this approach to reduce the need for contrast administration while preserving clinically relevant diagnostic information. This work should therefore be regarded as a feasibility study that establishes a clinically aligned ROI-focused framework for CESM virtual contrast synthesis and provides a foundation for future work.