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 preoperative
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.
KEYWORDS: Image registration, Finite element methods, Tumors, Tissues, Brain, Chemical elements, Neuroimaging, Medical imaging, Magnetic resonance imaging, Surgery
Intra-operative imaging during neurosurgical procedures facilitates aggressive resections and potentially an increased
surgical success rate compared to the traditional approach of relying purely on pre-operative data.
However, acquisition of functional images like fMRI and DTI still have to be performed pre-operatively which
necessitates registration to map them to the intra-operative image space. We present an elastic FEM-based registration
algorithm which is tailored to register pre-operative to intra-operative images where a superficial tumor
has been resected. To restrict matching of the cortical brain surface of the pre-operative image with the resected
cavity in the intra-operative image, we define a weight function based on the "concavity" of the deformation
field. These weights are applied to the load vector which effectively restricts the unwanted image forces around
the resected area from matching the brain surface in the pre-operative image with the surface of the resected
cavity. Another novelty of the proposed method is an adaptive multi-level FEM grid. After convergence of the
algorithm on one level, the FEM grid is subdivided to add more degrees of freedom to the deformation around
areas with a bad match. We present results from applying the algorithm on both 2D synthetic and medical image
data and can show that the adaptivity of the grid both improves registration results and registration speed while
the inclusion of the weighting function improves the results in the presence of resected tissue.
The European research network "Augmented reality in Surgery" (ARIS*ER) developed a system that supports
percutaneous radio frequency ablation of liver tumors. The system provides interventionists, during placement and
insertion of the RFA needle, with information from pre-operative CT images and real-time tracking data. A visualization
tool has been designed that aims to support (1) exploration of the abdomen, (2) planning of needle trajectory and (3)
insertion of the needle in the most efficient way. This work describes a first evaluation of the system, where user
performances and feedback of two visualization concepts of the tool - needle view and user view - are compared. After
being introduced to the system, ten subjects performed three needle placements with both concepts. Task fulfillment rate,
time for completion of task, special incidences, accuracy of needle placement recorded and analyzed. The results show
ambiguous results with beneficial and less favorable effects on user performance and workload of both concepts. Effects
depend on characteristics of intra-operative tasks as well as on task complexities depending on tumor location. The
results give valuable input for the next design steps.
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