We present a 3D extension and validation of an intra-operative registration framework that accommodates tissue resection. The framework is based on the bijective Demons method, but instead of regularizing with the traditional Gaussian smoother, we apply an anisotropic diffusion filter with the resection modeled as a diffusion sink. The diffusion sink prevents unwanted Demon forces that originates from the resected area from diffusing into the surrounding area. Another attractive property of the diffusion sink is the resulting continuous deformation field across the diffusion sink boundary, which allows us to move the boundary of the diffusion sink without changing values in the deformation field. The area of resection is estimated by a level-set method evolving in the space of image intensity disagreements in the intra-operative image domain. A product of using the bijective Demons method is that we can also provide an accurate estimate of the resected tissue in the pre-operative image space. Validation of the proposed method was performed on a set of 25 synthetic images. Our experiments show a significant improvement in accommodating resection using the proposed method compared to two other Demons based methods.
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