Evaluation of non-Gaussian statistical properties of digital breast tomosynthesis images: an initial study

To quantitatively study the non-Gaussian statistical properties of breast parenchymal texture on digital breast tomosynthesis (DBT) images acquired from two vendors. This IRB approved retrospective study included patients who had normal screening DBT exams performed on a GE unit in January 2018 and normal screening DBT exams in adjacent years (2017 or 2019) from a Hologic unit. We use Laplacian Fractional Entropy (LFE) as a measure of the non-Gaussian statistical properties from DBT images. Fifty-four Gabor filters that spanned 9 center frequencies from 0.15 to 2.0 cyc/mm were constructed at six different orientations (0 to150 deg). Filter responses were generated by convolving each filter with a Cranial-Caudal (CC) view DBT slice. All responses from 6 mm inside the boundary of the breast in each slice were used to form response histograms from which the LFE measures at different frequencies were estimated. The histograms binned the central 99% of the responses. The averaged LFE results among the central 80% of DBT slices were reported for each exam and compared between the two vendors. A total of 7,894 DBT slices from 69 exams in 25 women were included. Significant differences in LFE, were observed between DBT images acquired from Hologic and GE, using images from the same 25 patients in consecutive years. There are quantitative differences in non-Gaussian properties from the presentation of breast imaging texture between DBT vendors. Our findings have relevance and importance for external validation of AI algorithms across vendors.