title: Simulation of mammographic breast compression in 3D MR images using ICP-based B-spline deformation for multimodality breast cancer diagnosis.
abstract: OBJECTIVE
  Multimodality mammography using conventional 2D mammography and dynamic contrast-enhanced 3D magnetic resonance imaging (DCE-MRI) is frequently performed for breast cancer detection and diagnosis. Combination of both imaging modalities requires superimposition of corresponding structures in mammograms and MR images. This task is challenging due to large differences in (1) dimensionality and spatial resolution, (2) variations in tissue contrast, as well as (3) differences in breast orientation and deformation during the image acquisition. A new method for multimodality breast image registration was developed and tested.
  
  
  METHODS
  Combined diagnosis of mammograms and MRI datasets was achieved by simulation of mammographic breast compression to overcome large differences in breast deformation. Surface information was extracted from the 3D MR image, and back-projection of the 2D breast contour in the mammogram was done. B-spline-based 3D/3D surface-based registration was then used to approximate mammographic breast compression. This breast deformation simulation was performed on 14 MRI datasets with 19 corresponding mammograms. The results were evaluated by comparison with distances between corresponding structures identified by an expert observer.
  
  
  RESULTS
  The evaluation revealed an average distance of 6.46 mm between corresponding structures, when an optimized initial alignment between both image datasets is performed. Without the optimization, the accuracy is 9.12 mm.
  
  
  CONCLUSIONS
  A new surface-based method that approximates the mammographic deformation due to breast compression without using a specific complex model needed for finite-element-based methods was developed and tested with favorable results. The simulated compression can serve as foundation for a point-to-line correspondence between 2D mammograms and 3D MR image data.
authors.affiliation: Institute of Medical Informatics, University of Lübeck, Lübeck, Germany, krueger@imi.uni-luebeck.de.
authors.forename: Julia
authors.initials: J
authors.lastname: Krüger
authors.forename: Jan
authors.initials: J
authors.lastname: Ehrhardt
authors.forename: Arpad
authors.initials: A
authors.lastname: Bischof
authors.forename: Heinz
authors.initials: H
authors.lastname: Handels
mesh_terms: D001943:Breast Neoplasms / Q000175:diagnosis*; D003198:Computer Simulation; D044962:Data Compression / Q000379:methods*; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008327:Mammography / Q000379:methods*; D064847:Multimodal Imaging / Q000379:methods*; D015203:Reproducibility of Results