In this study, we propose a novel approach to dual-energy contrast-enhanced digital mammography, with the development of a dual-energy recombination algorithm based on an image chain model and the determination of the associated optimal low and high-energy techniques. Our method produces clutter-free iodine-equivalent images and includes thickness correction near the breast border. After the algorithm description, the optimal low and high-energy acquisition techniques are determined to obtain a compromise between image quality and glandular dose. The low and high-energy techniques were chosen to minimize the glandular dose for a target Signal Difference to Noise Ratio (SDNR) in the dual-energy recombined image. The theoretical derivation of the iodine SDNR in the recombined image allowed the prediction of the optimal low and high-energy techniques. Depending on the breast thickness and glandular percentage, the optimal low-energy kVp and mAs ranged from 24kVp (Mo/Mo or Mo/Rh) to 35kVp (Rh/Rh), and from 60 to 90mAs respectively, and the high-energy kVp and mAs ranged from 40kVp to 47kVp (Mo/Cu), and from 80mAs to 290mAs. We proved the better performance of our algorithm compared to the classic weighted logarithmic subtraction method in terms of patient dose and also in terms of texture cancelation, through the use of artificial textured images. Values of iodine contrast measured on phantom were close to the expected iodine thickness. Good correlation was found between the measured and theoretical iodine SDNR in the dual-energy images, which validates our theoretical optimization of the acquisition techniques.

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