Robotically Steered Needles: A Survey of Neurosurgical Applications and Technical Innovations.

This paper surveys both the clinical applications and main technical innovations related to steered needles, with an emphasis on neurosurgery. Technical innovations generally center on curvilinear robots that can adopt a complex path that circumvents critical structures and eloquent brain tissue. These advances include several needle-steering approaches, which consist of tip-based, lengthwise, base motion-driven, and tissue-centered steering strategies.

Deformable multisurface segmentation of the spine for orthopedic surgery planning and simulation.

We describe a shape-aware multisurface simplex deformable model for the segmentation of healthy as well as pathological lumbar spine in medical image data. This model provides an accurate and robust segmentation scheme for the identification of intervertebral disc pathologies to enable the minimally supervised planning and patient-specific simulation of spine surgery, in a manner that combines multisurface and shape statistics-based variants of the deformable simplex model. Statistical shape variation within the dataset has been captured by application of principal component analysis and incorporated during the segmentation process to refine results.

Medial axis segmentation of cranial nerves using shape statistics-aware discrete deformable models.

Purpose: We propose a segmentation methodology for brainstem cranial nerves using statistical shape model (SSM)-based deformable 3D contours from T MR images.

Methods: We create shape models for ten pairs of cranial nerves. High-resolution T MR images are segmented for nerve centerline using a 1-Simplex discrete deformable 3D contour model.

MRI-Based Medial Axis Extraction and Boundary Segmentation of Cranial Nerves Through Discrete Deformable 3D Contour and Surface Models.

This paper presents a segmentation technique to identify the medial axis and the boundary of cranial nerves. We utilize a 3-D deformable one-simplex discrete contour model to extract the medial axis of each cranial nerve. This contour model represents a collection of two-connected vertices linked by edges, where vertex position is determined by a Newtonian expression for vertex kinematics featuring internal and external forces, the latter of which include attractive forces toward the nerve medial axis.

3D lumbar spine intervertebral disc segmentation and compression simulation from MRI using shape-aware models.

Purpose: More accurate and robust image segmentations are needed for identification of spine pathologies and to assist with spine surgery planning and simulation. A framework for 3D segmentation of healthy and herniated intervertebral discs from T2-weighted magnetic resonance imaging was developed that exploits weak shape priors encoded in simplex mesh active surface models.

Methods: Weak shape priors inherent in simplex mesh deformable models have been exploited to automatically segment intervertebral discs.

Integration of a Multigrid ODE solver into an open medical simulation framework.

In this paper, we present the implementation of a Multigrid ODE solver in SOFA framework. By combining the stability advantage of coarse meshes and the transient detail preserving virtue of fine meshes, Multigrid ODE solver computes more efficiently than classic ODE solvers based on a single level discretization. With the ever wider adoption of the SOFA framework in many surgical simulation projects, introducing this Multigrid ODE solver into SOFA's pool of ODE solvers shall benefit the entire community.

Approach-specific multi-grid anatomical modeling for neurosurgery simulation with public-domain and open-source software.

We present on-going work on multi-resolution sulcal-separable meshing for approach-specific neurosurgery simulation, in conjunction multi-grid and Total Lagrangian Explicit Dynamics finite elements. Conflicting requirements of interactive nonlinear finite elements and small structures lead to a multi-grid framework. Implications for meshing are explicit control over resolution, and prior knowledge of the intended neurosurgical approach and intended path.