Automated Selection of Hypointense Regions in Diffusion-Weighted Breast MRI

Darryl McClymont¹, Andrew Mehnert², Adnan Trakic¹, Dominic Kennedy³, Stuart Crozier¹

Recent research suggests that diffusion-weighted MRI can be used to improve the sensitivity and specificity of dynamic contrast-enhanced MRI for the detection of breast cancer. However, to date the methods proposed for determining a representative ADC value for a suspicious lesion are highly varied. We propose an automated method based on the converging squares algorithm, which is a noise-robust minimum finding technique. We present an evaluation of the method for computing a representative ADC. The method is also compared to ensemble averaging of ADC values over the entire lesion and the selection of the global minimum ADC value.