Breast cancer is the most frequently diagnosed cancer and the most common cause of cancer-related death in Australian women. The most recent national figures show that a woman has a 1 in 9 chance of being diagnosed with breast cancer before the age of 85. They also show that each year more than 13,600 women are diagnosed with breast cancer and that more than 2800 die from the disease. 


X-ray mammography plays a key role in breast cancer screening programs worldwide. Nevertheless, the test is known to have several shortcomings including an overall false-negative rate of about 20% and a low sensitivity to small cancers.

Magnetic resonance imaging (MRI) is an alternative imaging modality that shows promise for improved breast cancer screening. Significantly, in comparison to existing tests it has been shown to have possibly the highest sensitivity to invasive cancer and multifocal disease. It is also the most reliable method for assessing tumour size and extent, compared to gold standard histopathology. The disadvantage of MRI, however, is that its specificity is poor; only 1 in 3 cases recommended for biopsy for a suspicious MRI finding actually have a cancer.

This research aims to improve the specificity (and possibly sensitivity) of breast MRI, and therefore its clinical utility, by integrating information about tissue enhancement, morphology, and microstructure obtained from spatially aligned intrinsic contrast T1- and T2- weighted images, dynamic contrast enhanced T1-weighted images, and diffusion-weighted images; and by reducing the subjectivity in routine clinical interpretation of breast MRI data by means of computer visualisation, haptic interaction, image analysis, and pattern recognition based on quantitative measurements of the MRI data.

KEY PUBLICATIONS:

  1. McClymont, D, Mehnert, A, Trakic, A, Kennedy, D, Crozier, S (2011): A Novel Method for Automatic Extraction of Apparent Diffusion Coefficients in Breast MRI, in Proceedings ISMRM 2011, Montreal, Canada. 

  2. Mehnert, A, Wildermoth, M, Crozier, S, Bengtsson, E, Kennedy, D (2011): Two non-linear parametric models of enhancement for breast DCE-MRI that can be fitted using linear least squares, in Proceedings ISMRM 2011, Montreal, Canada. 

  3. Y. Gal, A. Mehnert, A. Bradley, D. Kennedy, S. Crozier. Feature and classifier selection for automatic classification of lesions in dynamic contrast-enhanced MRI of the breast. In: Proceedings of the International Conference on Digital Image Computing: Techniques and Applications, DICTA 2009, Melbourne, Australia, pp. 132-139, 1-3 December 2009.