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论文类型：期刊论文

发表时间：2015-12-01

发表刊物：COMPUTERIZED MEDICAL IMAGING AND GRAPHICS

收录刊物：SCIE、EI、PubMed、Scopus

卷号：46

期号：,SI

页面范围：73-80

ISSN号：0895-6111

关键字：Image registration; Sparsity; DCE-MRI; Breast cancer

摘要：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 L-1 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. (C) 2015 Elsevier Ltd. All rights reserved.