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MRI to X-ray mammography registration using a volume-preserving affine transformation

T. Mertzanidou, J. Hipwell, M. J. Cardoso, X. Zhang, C. Tanner, S. Ourselin, U. Bick, H. Huisman, N. Karssemeijer, D. Hawkes
Medical Image Analysis
Vol. 16, No. 5, pp. 966, 2012


X-ray mammography is routinely used in national screening programmes and as a clinical diagnostic tool. Magnetic Resonance Imaging (MRI) is commonly used as a complementary modality, providing functional information about the breast and a 3D image that can overcome ambiguities caused by the superimposition of fibro-glandular structures associated with X-ray imaging. Relating findings between these modalities is a challenging task however, due to the different imaging processes involved and the large deformation that the breast undergoes. In this work we present a registration method to determine spatial correspondence between pairs of MR and X-ray images of the breast, that is targeted for clinical use. We propose a generic registration framework which incorporates a volume-preserving affine transformation model and validate its performance using routinely acquired clinical data. Experiments on simulated mammograms from 8 volunteers produced a mean registration error of 3.8+-1.6 mm for a mean of 12 manually identified landmarks per volume. When validated using 57 lesions identified on routine clinical CC and MLO mammograms (n = 113 registration tasks) from 49 subjects the median registration error was 13.1 mm. When applied to the registration of an MR image to CC and MLO mammograms of a patient with a localisation clip, the mean error was 8.9 mm. The results indicate that an intensity based registration algorithm, using a relatively simple transformation model, can provide radiologists with a clinically useful tool for breast cancer diagnosis.

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  author = {T. Mertzanidou and J. Hipwell and M. J. Cardoso and X. Zhang and C. Tanner and S. Ourselin and U. Bick and H. Huisman and N. Karssemeijer and D. Hawkes},
  title = {MRI to X-ray mammography registration using a volume-preserving affine transformation},
  journal = {Medical Image Analysis},
  year = {2012},
  month = {},
  pages = {966},
  volume = {16},
  number = {5},
  keywords = {}