title: Simultaneous multiple image registration method for T1 estimation in breast MRI images.
abstract: The estimation and subsequent use of tissue T1(x) parameters at each image location x can potentially lead to a more reliable classification of breast tissues. T1 values can be estimated using multiple (typically 3) MRI images of different flip angles. However, breathing and other slight movements can render the highly non-linear estimation procedure error-prone. In this paper, a simultaneous multiple image registration method is proposed to solve this problem. The registration method is built upon the idea of conserving inverse consistency and transitivity among the multiple image transformations. The algorithm is applied to both simulated data and real breast MRI images. The performance is compared with existing pairwise image registration method. The results clearly indicate that the simultaneous multiple image registration algorithm leads to much more accurate T1 estimation.
authors.affiliation: Wolfson Medical Vision Laboratory, University of Oxford, Oxford, UK. jlo@robots.ox.ac.uk
authors.forename: Jonathan Lok-Chuen
authors.initials: JL
authors.lastname: Lo
authors.forename: Michael
authors.initials: M
authors.lastname: Brady
authors.forename: Niall
authors.initials: N
authors.lastname: Moore
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001940:Breast / Q000473:pathology*; D001943:Breast Neoplasms / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique