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Purpose. Report advances being made in synthetic mammography applied to carbon nanotube-enabled stationary digital breast tomosynthesis (sDBT). Methods. The potential value of adding Laplacian decomposition, feature-enhancement algorithms, and weighted recombination to the tunable forward-projection steps developed previously to generate synthetic mammograms for sDBT was studied in this phantom-based comparison of sDBT to full field digital mammography (FFDM) and moving-source or conventional DBT. Contrast-to-noise ratio (CNR) and the full-width-at-half-maximum (FWHM) of the signal intensity were used to compare the display of microcalcification and mass features in the FFDM image and the synthetic images generated by sDBT and DBT. These findings guided modifications in the sDBT image processing chain, seeking to maximize the display of clinically-important features in the sDBT-based synthetic image. Results. Decomposing each reconstructed image slice into its high, mid, and low-frequency components yielded images emphasizing a different feature of clinical importance: microcalcifications, masses, and background density. Applying feature-enhancement algorithms to these images followed by weighted recombination during forward projection yielded an sDBT-based synthetic image that displayed masses with a higher CNR than the FFDM image and the synthetic image generated by DBT. Additionally, microcalcifications that could be visualized in all three modalities were displayed with a higher CNR in the synthetic images generated by DBT and sDBT compared to the FFDM image. Conclusion. Adding Laplacian decomposition, feature-enhancement, and weighted recombination steps to the image processing chain that generates a synthetic image from information collected by sDBT improved the display of clinicallyimportant features. Advancing the synthetic mammography capability of sDBT is important, as it will help complete the evolution of this promising technology to a viable clinical tool.
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