Work

Frequency-based texture analysis of non-Gaussian properties of digital breast tomosynthesis images and comparison across two vendors

Year: 2025

Type: article

Abstract: PurposeWe aim to analyze higher-order textural components of digital breast tomosynthesis (DBT) images to quantify differences in the appearance of breast parenchyma produced by different vendors.ApproachWe included consecutive women who had normal screening DBT exams in January 2018 from a GE system and in adjacent years from Hologic systems. Laplacian fractional entropy (LFE), as a measure of non-Gaussian statistical properties of breast tissue texture, was calculated from for-presentation Craniocaudal (CC) view DBT slices and synthetic mammograms (SMs) through frequency-based filtering with Gabor filters, which were considered mathematical models for human visual response to image textures. The LFE values were compared within and across subjects and vendors along with secondary parameters (laterality, year-to-year, modality, and breast density) via two-way analysis of variance (ANOVA) tests using frequency as one of the two independent variables, and a P-value <0.05 was considered statistically significant.ResultsA total of 8529 CC view DBT slices and SM images from 73 screening exams in 25 women were analyzed. Significant differences in LFE were observed for different frequencies (P<0.001) and across vendors (GE versus Hologic DBT: P<0.001, GE versus Hologic SM: P<0.001).ConclusionSignificant differences in perception of breast parenchyma textures among two DBT vendors were demonstrated via higher-order non-Gaussian statistical properties. This finding extends previously observed differences in anatomical noise power spectra in DBT images and provides quantitative evidence to support caution in across-vendor comparative reading and will be beneficial to facilitate future development of vendor-neutral artificial intelligence algorithms for breast cancer screening.

Source: Journal of medical imaging

DOI: 10.1117/1.jmi.12.s2.s22004

Authors: Kai Yang, Craig K. Abbey, Bruno Barufaldi, Xinhua Li, Theodore A. Marschall, Baohua Liu

Institutions: Massachusetts General Hospital, University of California, Santa Barbara, University of Pennsylvania

Language: English

Topic: Digital Radiography and Breast Imaging

Subfield: Pulmonary and Respiratory Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: Susan G. Komen for the Cure, Terri Brodeur Breast Cancer Foundation