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Contrast-enhanced mammography (CEM) offers a promising alternative to address the limitations of digital mammography, particularly in cases of dense breast tissue, which compromises the performance of non-contrast x-ray imaging modalities. CEM uses iodinated contrast material to enhance cancer detection in denser breasts and provides critical functional information about suspicious findings. However, the process of combining images acquired with different x-ray energy spectra in CEM can introduce artifacts, challenging interpretation and confidence in CEM images. This study presents novel approaches to improve CEM image quality. First, deep learning (DL)-based algorithms for scatter correction in both low-energy and high-energy images are proposed to enhance contrast-enhancement patterns and iodine quantification. Additionally, a unique deep learning network is introduced to predict pixel-by-pixel the compressed breast thickness, enabling the use of local thickness-based image subtraction-weighting maps throughout the breast area. Results in phantom cases demonstrate the effectiveness of the scatter correction models in predicting the scatter signal, even in cases with the anti-scatter grid present. The thickness map model accurately estimates the local thickness, particularly in the constant thickness area of the breast. Comparison with clinical data revealed good agreement between estimated thickness maps and ground truth, with minor discrepancies attributed to alignment issues. Furthermore, the study explored the combined use of scatter correction and thickness-based weighting maps in creating recombined CEM images. This approach showed a marginal positive impact due to scatter correction, with larger improvements observed in the signal intensity homogeneity at the border of the breast. These advancements aim to enhance the CEM diagnostic accuracy, making it a valuable tool for breast cancer detection and evaluation, especially in cases with dense breast tissue.
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