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Publication Detail
MRI to X-ray mammography registration using a volume-preserving affine transformation.
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Journal Article
  • Authors:
    Mertzanidou T, Hipwell J, Cardoso MJ, Zhang X, Tanner C, Ourselin S, Bick U, Huisman H, Karssemeijer N, Hawkes D
  • Publication date:
    07/2012
  • Pagination:
    966, 975
  • Journal:
    Med Image Anal
  • Volume:
    16
  • Issue:
    5
  • Status:
    Published
  • Country:
    Netherlands
  • PII:
    S1361-8415(12)00046-1
  • Language:
    eng
  • Keywords:
    Algorithms, Breast Neoplasms, Female, Humans, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Magnetic Resonance Imaging, Mammography, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, Subtraction Technique
Abstract
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±1.6mm 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 13.1mm. 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 8.9mm. 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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