title: MRI to X-ray mammography registration using a volume-preserving affine transformation.
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.
authors.affiliation: Centre for Medical Image Computing, University College London, Gower Street, London, UK. t.mertzanidou@cs.ucl.ac.uk
authors.forename: Thomy
authors.initials: T
authors.lastname: Mertzanidou
authors.forename: John
authors.initials: J
authors.lastname: Hipwell
authors.forename: M Jorge
authors.initials: MJ
authors.lastname: Cardoso
authors.forename: Xiying
authors.initials: X
authors.lastname: Zhang
authors.forename: Christine
authors.initials: C
authors.lastname: Tanner
authors.forename: Sebastien
authors.initials: S
authors.lastname: Ourselin
authors.forename: Ulrich
authors.initials: U
authors.lastname: Bick
authors.forename: Henkjan
authors.initials: H
authors.lastname: Huisman
authors.forename: Nico
authors.initials: N
authors.lastname: Karssemeijer
authors.forename: David
authors.initials: D
authors.lastname: Hawkes
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008327:Mammography / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique