An automated approach for the optimised estimation of breast density with Dixon methods

To present and evaluate an automated method to correct scaling between Dixon water/fat images used in breast density (BD) assessments.

Dixon images were acquired in 14 subjects with different T₁ weightings (flip angles, FA, 4°/16°). Our method corrects intensity differences between water ($$) and fat ($$) images via the application of a uniform scaling factor (SF), determined subject-by-subject. Based on the postulation that optimal SFs yield relatively featureless summed fat/scaled-water ($$) images, each SF was chosen as that which generated the lowest 95th-percentile in the absolute spatial-gradient image-volume of $$ . Water-fraction maps were calculated for data acquired with low/high FAs, and BD (%) was the total percentage water within each breast volume.

Corrected/uncorrected BD ranged from, respectively, 10.9-71.8%/8.9-66.7% for low-FA data to 8.1-74.3%/5.6-54.3% for high-FA data. Corrected metrics had an average absolute increase in BD of 6.4% for low-FA data and 18.4% for high-FA data. BD values estimated from low- and high-FA data were closer following SF-correction.

Our results demonstrate need for scaling in such BD assessments, where our method brought high-FA and low-FA data into closer agreement.

We demonstrated a feasible method to address a main source of inaccuracy in Dixon-based BD measurements.