期刊
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
卷 46, 期 -, 页码 73-80出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compmedimag.2015.05.004
关键词
Image registration; Sparsity; DCE-MRI; Breast cancer
资金
- Natural Science Foundation of Shandong Province [ZR2014FM001, ZR2011FQ033]
- Taishan Scholar Program of Shandong Province
- Shandong Normal University
- Institute Support Innovation Project of Jinan Administration of Science Technology [201202012, 201303004]
- Fundamental Research Funds for the Central Universities of China
- National Institutes of Health (NIH) [NS045839, HHSN276201000492P]
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.
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