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MRI – Mammography 2D/3D Data Fusion for Breast Pathology Assessment

  • Christian P. Behrenbruch
  • Kostas Marias
  • Paul A. Armitage
  • Margaret Yam
  • Niall Moore
  • Ruth E. English
  • J. Michael Brady
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1935)

Abstract

Increasing use is being made of contrast-enhanced Magnetic Resonance Imaging (Gd-DTPA) for breast cancer assessment since it provides 3D functional information via pharmacokinetic interaction between contrast agent and tumour vascularity, and because it is applicable to women of all ages. Contrast-enhanced MRI (CE-MRI) is complimentary to conventional X-ray mammography since it is a relatively low-resolution functional counterpart of a comparatively high-resolution 2D structural representation. However, despite the additional information provided by MRI, mammography is still an extremely important diagnostic imaging modality, particularly for several common conditions such as ductal carcinoma in-situ (DCIS) where it has been shown that there is a strong correlation between microcalcification clusters and malignancy [1]. Pathological indicators such as calcifications and fine spiculations are not visible in CE-MRI and therefore there is clinical and diagnostic value to fusing the high-resolution structural information available from mammography with the functional data acquired from MRI imaging. This paper presents a novel data fusion technique whereby medio-lateral (ML) and cranio-caudal (CC) mammograms (2D data) are registered to 3D contrast-enhanced MRI volumes. We utilise a combination of pharmacokinetic modelling, projection geometry, wavelet-based landmark detection and thin-plate spline non-rigid registration to transform the coordinates of regions of interest (ROIs) from the 2D mammograms to the spatial reference frame of the contrast-enhanced MRI volume.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Christian P. Behrenbruch
    • 1
  • Kostas Marias
    • 1
    • 2
  • Paul A. Armitage
    • 1
  • Margaret Yam
    • 1
  • Niall Moore
    • 3
  • Ruth E. English
    • 4
  • J. Michael Brady
    • 1
  1. 1.Medical Vision Laboratory (Robotics), Engineering ScienceOxford UniversityOxfordUK
  2. 2.Department of SurgeryRoyal Free and University College Medical School, UCLLondonUK
  3. 3.Magnetic Resonance Imaging CentreJohn Radcliffe HospitalOxfordUK
  4. 4.Breast Care UnitChurchill HospitalOxfordUK

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