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Abstract #0845

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

Isobel Gordon1,2, George Ralli2, Carolina Fernandes2, Amy Herlihy2, Sally Collins1, and Michael Brady2,3
1Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom, 2Perspectum Ltd., Oxford, United Kingdom, 3Engineering Science, University of Oxford, Oxford, United Kingdom

Synopsis

Keywords: Breast, Phantoms, Quantitative, fat fraction

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.

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