Compared to traditional magnetic resonance imaging, which provides anatomical information with high contrast, diffusion weighted imaging (DWI) can add functional information for a more precise detection and localization of breast cancer. However, DWI may suffer from artifacts due to off-resonance effects, including geometric distortions. This hinders combined view, e.g. by image fusion. In this work, we investigate a distortion correction of DWI based on a nonlinear image registration with a T2 weighted image. Our method consists of three steps: a data cleaning step in which differences in image sections and resolution are compensated, an edge detection step which extracts the outline and inner structures of the breast in both DWI and T2 weight image, and finally a non-rigid registration step using the demons algorithm. We use two clinical datasets with a total of seven patients for evaluation. Manual annotations of landmarks in 227 slices serve as basis to calculate the registration error. Our method reduces the target registration error based on the center of gravity of annotations from in average 5.5 mm to 3.1 mm and is most effective in cases with large initial deformation. Compared to the other methods tested in this study the proposed method shows the lowest error. The method may contribute to a better combined diagnosis and e.g. facilitate computer aided detection and diagnosis by enabling combination of spatially well-aligned information.
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