Adaptive Physics-Based Non-Rigid Registration for Immersive Image-Guided Neuronavigation Systems

Abstract

Objective: In image-guided neurosurgery, co-registered preoperative anatomical, functional, and diffusion tensor imaging can be used to facilitate a safe resection of brain tumors in eloquent areas of the brain. However, the brain deforms during surgery, particularly in the presence of tumor resection. Non-Rigid Registration (NRR) of the preoperative image data can be used to create a registered image that captures the deformation in the intraoperative image while maintaining the quality of the preoperative image. Using clinical data, this paper reports the results of a comparison of the accuracy and performance among several non-rigid registration methods for handling brain deformation. A new adaptive method that automatically removes mesh elements in the area of the resected tumor, thereby handling deformation in the presence of resection is presented. To improve the user experience, we also present a new way of using mixed reality with ultrasound, MRI, and CT.Materials and methods: This study focuses on 30 glioma surgeries performed at two different hospitals, many of which involved the resection of significant tumor volumes. An Adaptive Physics-Based Non-Rigid Registration method (A-PBNRR) registers preoperative and intraoperative MRI for each patient. The results are compared with three other readily available registration methods: a rigid registration implemented in 3D Slicer v4.4.0; a B-Spline non-rigid registration implemented in 3D Slicer v4.4.0; and PBNRR implemented in ITKv4.7.0, upon which A-PBNRR was based. Three measures were employed to facilitate a comprehensive evaluation of the registration accuracy: (i) visual assessment, (ii) a Hausdorff Distance-based metric, and (iii) a landmark-based approach using anatomical points identified by a neurosurgeon.Results: The A-PBNRR using multi-tissue mesh adaptation improved the accuracy of deformable registration by more than five times compared to rigid and traditional physics based non-rigid registration, and four times compared to B-Spline interpolation methods which are part of ITK and 3D Slicer. Performance analysis showed that A-PBNRR could be applied, on average, in <2 min, achieving desirable speed for use in a clinical setting.Conclusions: The A-PBNRR method performed significantly better than other readily available registration methods at modeling deformation in the presence of resection. Both the registration accuracy and performance proved sufficient to be of clinical value in the operating room. A-PBNRR, coupled with the mixed reality system, presents a powerful and affordable solution compared to current neuronavigation systems.

Computer simulation of tumour resection‐induced brain deformation by a meshless approach

“…In patient‐specific computational biomechanics modelling, the material properties are typically assigned using image segmentation 12,14–16 . In image segmentation, a single tissue label is assigned to each voxel 37 .…”

“…In patient-specific computational biomechanics modelling, the material properties are typically assigned using image segmentation. 12,[14][15][16] In image segmentation, a single tissue label is assigned to each voxel. 37 For the brain and other organs affected by pathological changes, such 'hard' segmentation tends to be a very time-consuming manual process.…”

“…Conventional patient-specific biomechanical modelling of brain shift has historically relied on the finite element method (FEM) for solving differential equations governing the biomechanical behaviour of the brain and image segmentation to assign material properties to different tissue types. 12,[14][15][16] This makes creating patient-specific biomechanical models for complex geometry like the brain very time-consuming. Tetrahedral meshes are easier to generate, but it is very difficult to obtain efficiently robust and accurate solutions for almost incompressible material like the brain.…”

Automatic framework for patient-specific modelling of tumour resection-induced brain shift

“…Conventional patient-specific biomechanical modelling of the brain shift has historically relied on the finite element method (FEM) [3,6,[15][16][17][18][19][20]. However, the construction (including image segmentation and meshing) of a comprehensive brain model that includes parenchyma, tumour, ventricles, and skull requires a high level of expertise and many days of tedious work.…”

“…However, the construction (including image segmentation and meshing) of a comprehensive brain model that includes parenchyma, tumour, ventricles, and skull requires a high level of expertise and many days of tedious work. Using tetrahedral elements in the biomechanical brain model is preferred by many researchers [17,18], but it is less efficient and robust compared to a model using hexahedral or hexahedral dominated meshes [16,21]. Recently, our research group (Intelligent Systems for Medicine Laboratory) developed and implemented computational biomechanics models and nonlinear meshless algorithms (Meshless Total Lagrangian Explicit Dynamic, abbreviated as MTLED) for the computation of brain deformation [22,23].…”