title: Measuring sparse temporal-variation for accurate registration of dynamic contrast-enhanced breast MR images.
abstract: Accurate registration of dynamic contrast-enhanced (DCE) MR breast images is challenging due to the temporal variations of image intensity and the non-rigidity of breast motion. The former can cause the well-known tumor shrinking/expanding problem in registration process while the latter complicates the task by requiring an estimation of non-rigid deformation. In this paper, we treat the intensity's temporal variations as "corruptions" which spatially distribute in a sparse pattern and model them with a L1 norm and a Lorentzian norm. We show that these new image similarity measurements can characterize the non-Gaussian property of the difference between the pre-contrast and post-contrast images and help to resolve the shrinking/expanding problem by forgiving significant image variations. Furthermore, we propose an iteratively re-weighted least squares based method and a linear programming based technique for optimizing the objective functions obtained using these two novel norms. We show that these optimization techniques outperform the traditional gradient-descent approach. Experimental results with sequential DCE-MR images from 28 patients show the superior performances of our algorithms.
authors.affiliation: School of Information Science and Engineering, Shandong Normal University, Jinan, Shandong, China; Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA. Electronic address: yjzheng@sdnu.edu.cn.
authors.forename: Yuanjie
authors.initials: Y
authors.lastname: Zheng
authors.affiliation: College of Science and Engineering, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.
authors.forename: Benzheng
authors.initials: B
authors.lastname: Wei
authors.affiliation: Department of Electronic Engineering, Dalian University of Technology, Dalian, Liaoning, China.
authors.forename: Hui
authors.initials: H
authors.lastname: Liu
authors.affiliation: Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
authors.forename: Rui
authors.initials: R
authors.lastname: Xiao
authors.affiliation: Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
authors.forename: James C
authors.initials: JC
authors.lastname: Gee
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000473:pathology*; D003287:Contrast Media; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods* / Q000592:standards; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D012680:Sensitivity and Specificity
chemical_list: D003287:Contrast Media
keywords: Breast cancer; DCE-MRI; Image registration; Sparsity