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Erschienen in:
2007 | OriginalPaper | Buchkapitel
De-enhancing the Dynamic Contrast-Enhanced Breast MRI for Robust Registration
verfasst von : Yuanjie Zheng, Jingyi Yu, Chandra Kambhamettu, Sarah Englander, Mitchell D. Schnall, Dinggang Shen
Erschienen in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007
Verlag: Springer Berlin Heidelberg
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Dynamic enhancement causes serious problems for registration of contrast enhanced breast MRI, due to variable uptakes of agent on different tissues or even same tissues in the breast. We present an iterative optimization algorithm to de-enhance the dynamic contrast-enhanced breast MRI and then register them for avoiding the effects of enhancement on image registration. In particular, the spatially varying enhancements are modeled by a Markov Random Field, and estimated by a locally smooth function with boundaries using a graph cut algorithm. The de-enhanced images are then registered by conventional B-spline based registration algorithm. These two steps benefit from each other and are repeated until the results converge. Experimental results show that our two-step registration algorithm performs much better than conventional mutual information based registration algorithm. Also, the effects of tumor shrinking in the conventional registration algorithms can be effectively avoided by our registration algorithm.