title: Image fusion for dynamic contrast enhanced magnetic resonance imaging.
abstract: BACKGROUND
  Multivariate imaging techniques such as dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) have been shown to provide valuable information for medical diagnosis. Even though these techniques provide new information, integrating and evaluating the much wider range of information is a challenging task for the human observer. This task may be assisted with the use of image fusion algorithms.
  
  
  METHODS
  In this paper, image fusion based on Kernel Principal Component Analysis (KPCA) is proposed for the first time. It is demonstrated that a priori knowledge about the data domain can be easily incorporated into the parametrisation of the KPCA, leading to task-oriented visualisations of the multivariate data. The results of the fusion process are compared with those of the well-known and established standard linear Principal Component Analysis (PCA) by means of temporal sequences of 3D MRI volumes from six patients who took part in a breast cancer screening study.
  
  
  RESULTS
  The PCA and KPCA algorithms are able to integrate information from a sequence of MRI volumes into informative gray value or colour images. By incorporating a priori knowledge, the fusion process can be automated and optimised in order to visualise suspicious lesions with high contrast to normal tissue.
  
  
  CONCLUSIONS
  Our machine learning based image fusion approach maps the full signal space of a temporal DCE-MRI sequence to a single meaningful visualisation with good tissue/lesion contrast and thus supports the radiologist during manual image evaluation.
authors.affiliation: Applied Neuroinformatics Group, Faculty of Technology, University of Bielefeld, Germany. ttwellma@TechFak.Uni-Bielefeld.de
authors.forename: Thorsten
authors.initials: T
authors.lastname: Twellmann
authors.forename: Axel
authors.initials: A
authors.lastname: Saalbach
authors.forename: Olaf
authors.initials: O
authors.lastname: Gerstung
authors.forename: Martin O
authors.initials: MO
authors.lastname: Leach
authors.forename: Tim W
authors.initials: TW
authors.lastname: Nattkemper
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000175:diagnosis; D003287:Contrast Media; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D025341:Principal Component Analysis
chemical_list: D003287:Contrast Media