A topologically faithful, tissue-guided, spatially varying meshing strategy for computing patient-specific head models for endoscopic pituitary surgery simulation.

This paper presents a method for tessellating tissue boundaries and their interiors, given as input a map consisting of relevant tissue classes of the head, in order to produce anatomical models for finite-element-based simulation of endoscopic pituitary surgery. Our surface meshing method is based on the simplex model, which is initialized by duality from the topologically accurate results of the Marching Cubes algorithm, and which affords explicit control over mesh scale, while using tissue information to adhere to relevant boundaries. Our mesh scale strategy is spatially varying, based on the distance to a central point or linearized surgical path. The tetrahedralization stage also features a spatially varying mesh scale, consistent with that of the surface mesh.

A topologically faithful, tissue-guided, spatially varying meshing strategy for the computation of patient-specific head models for endoscopic pituitary surgery simulation.

This paper presents a method for tessellating tissue boundaries and their interiors, given as input a tissue map consisting of relevant classes of the head, in order to produce anatomical models for finite element-based simulation of endoscopic pituitary surgery. Our surface meshing method is based on the simplex model, which is initialized by duality from the topologically accurate results of the Marching Cubes algorithm, and which features explicit control over mesh scale, while using tissue information to adhere to relevant boundaries. Our mesh scale strategy is spatially varying, based on the distance to a central point or linearized surgical path. The tetrahedralization stage also features a spatially varying mesh scale, consistent with that of the surface mesh.

An algorithmic overview of surface registration techniques for medical imaging.

This paper presents a literature survey of automatic 3D surface registration techniques emphasizing the mathematical and algorithmic underpinnings of the subject. The relevance of surface registration to medical imaging is that there is much useful anatomical information in the form of collected surface points which originate from complimentary modalities and which must be reconciled. Surface registration can be roughly partitioned into three issues: choice of transformation, elaboration of surface representation and similarity criterion, and matching and global optimization. The first issue concerns the assumptions made about the nature of relationships between the two modalities, e.g. whether a rigid-body assumption applies, and if not, what type and how general a relation optimally maps one modality onto the other. The second issue determines what type of information we extract from the 3D surfaces, which typically characterizes their local or global shape, and how we organize this information into a representation of the surface which will lead to improved efficiency and robustness in the last stage. The last issue pertains to how we exploit this information to estimate the transformation which best aligns local primitives in a globally consistent manner or which maximizes a measure of the similarity in global shape of two surfaces. Within this framework, this paper discusses in detail each surface registration issue and reviews the state-of-the-art among existing techniques.