Automated analysis of contrast enhancement in magnetic resonance imaging of the breast
Promotor: J. O. Barentsz, N. Karssemeijer and C. Boetes Copromotor: H. J. Huisman Radboud University, Nijmegen 2011
This work covers research on the application of breast MRI and on computer aided diagnosis (CAD), aimed at improving the accuracy of the imaging interpretation. First, a study is described that was designed to answer whether magnetic resonance imaging (MRI) can be used to periodically examine women with a hereditary increased risk of breast cancer. MRI appeared more accurate than conventional mammography, when used in annual breast cancer surveillance of women with an increased hereditary risk of breast cancer. The thesis then describes the observer variability in reporting lesion features, including an approach to improve it. Surprisingly, a considerable variability was found in the use of most generally accepted terms. The preparation of a region of interest for analysis of contrast enhancement turned out to be a major source of variability in the interpretation of enhancement curves. Technological advances made it possible to run both very fast imaging series and slower, high-definition series, within the same breast MRI study. Such a hybrid set of series was used in a clinical study performed by us to determine if slow, fast or a combination of series would result in the best diagnostic performance. We concluded that a combination of series yielded the best results. Next is the description of a method for automated analysis of contrast enhancement in breast MRI (mean shift clustering followed by iterative selection of the region of interest) . Finally, an improvement of our CAD method is described. This thesis describes that: 1) Breast MRI can be used for periodical surveillance of women with hereditary increased risk of breast cancer; 2) Breast MRI suffers from limited specificity of breast MRI, partly caused by the inter-observer variability in the placement of the region of interest for pharmacokinetic analysis; 3) Our CAD method is a feasible technique to automatically place the region of interest and to obtain a probability of malignancy, especially if expanded with pharmacokinetic modeling.