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 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.