De-enhancing the dynamic contrast-enhanced breast MRI for robust registration

Authors:
Yuanjie Zheng;Jingyi Yu;Chandra Kambhamettu;Sarah Englander;Mitchell D. Schnall;Dinggang Shen
Affiliations:
Department of Radiology, University of Pennsylvania, Philadelphia, PA and Department of Computer and Information Sciences, University of Delaware, Newark, DE;Department of Computer and Information Sciences, University of Delaware, Newark, DE;Department of Computer and Information Sciences, University of Delaware, Newark, DE;Department of Radiology, University of Pennsylvania, Philadelphia, PA;Department of Radiology, University of Pennsylvania, Philadelphia, PA;Department of Radiology, University of Pennsylvania, Philadelphia, PA
Venue:
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Year:
2007

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Abstract

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.