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Abstract #1845

Automated Selection of Hypointense Regions in Diffusion-Weighted Breast MRI

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

1University of Queensland, Brisbane, QLD, Australia; 2Chalmers University of Technology, Sweden; 3Queensland X-Ray, Australia

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.