EVENT: CMCA Seminar Series: Computer-aided detection/diagnosis of breast cancer in multi-modal MRI

A recent meta-study concluded that the use of computer-aided detection/diagnosis (CAD) "in breast MRI has little influence on the sensitivity and specificity of experienced radiologists and therefore their interpretation remains essential". In this talk I describe ongoing collaborative research to improve the sensitivity and specificity of breast MRI, and concomitantly its clinical utility, by combining multi-modal MRI with novel multi-parametric and multi-dimensional image analysis techniques. In particular we are developing methods to quantitatively characterise tissue morphology, microvasculature, and microstructure from spatially aligned multi-modal MR images including anatomical T1- and T2-weighted images, as well as images acquired using dynamic contrast-enhanced (DCE) MRI, and diffusion-weighted imaging. We are also developing image analysis methods to automatically extract these features, segment (delineate) suspicious tissue, and classify the tissue as benign or malignant. Results to date include a novel registration evaluation framework based on a biomechanical breast model that permits realistic simulation of tissue deformation, new spatiotemporal features for improved discrimination of benign and malignant lesions in DCE-MRI, and most recently the first fully automatic method for breast lesion detection and delineation.