In-vitro evaluation of the performance of PDFF against classification-based algorithms in calculation of breast density

Motivation: Operator-independent quantification of breast density with proton density fat fraction (PDFF) may be more accurate than conventional T1-weighted imaging-based methods, which are limited by the partial volume effect (PVE) and require significant user input. Goal(s): We aimed to assess the accuracy of PDFF against fuzzy clustering (FCM) of T1-weighted images. Approach: Five phantoms representative of different breast compositions were imaged and the breast density calculated with both methods was compared to the known density. Results: PDFF demonstrated improved accuracy compared to FCM of T1-weighted images. FCM-derived density was more sensitive to the partial volume effect and dependent on the bias correction algorithm. Impact: The improved accuracy and comparative robustness of proton density fat fraction (PDFF) suggests it is a more reliable and operator-independent approach to breast density calculation than fuzzy clustering. This is particularly important when assessing longitudinal changes to breast structure.