A parallel method for topological transformations for local reconnection methods is presented. The proposed scheme combines known parallel techniques like data over-decomposition and load balancing with widely used topological transformations also known as flips or swaps. In contrast to most mesh generation methods, the proposed optimizes the connectivity in parallel throughout the mesh generation procedure. The speculative scheme is evaluated on a variety of aerospace configurations. Early results indicate that the high quality and performance attributes of this method see substantial improvement over existing state-of-the-art technology.

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.

In this paper, we present a scalable three-dimensional hybrid parallel Delaunay image-to-mesh conversion algorithm (PDR.PODM) for distributed shared memory architectures. PDR.PODM is able to explore parallelism early in the mesh generation process because of the aggressive speculative approach employed by the Parallel Optimistic Delaunay Mesh generation algorithm (PODM). In addition, it decreases the communication overhead and improves data locality by making use of a data partitioning scheme offered by the Parallel Delaunay Refinement algorithm (PDR). PDR.PODM supports fully functional volume grading by creating elements with varying size. Small elements are created near boundary or inside the critical regions in order to capture the fine features while big elements are created in the rest of the mesh. We tested PDR.PODM on Blacklight, a distributed shared memory (DSM) machine in Pittsburgh Supercomputing Center. For the uniform mesh generation, we observed a weak scaling speedup of 163.8 and above for up to 256 cores as opposed to PODM whose weak scaling speedup is only 44.7 on 256 cores. The end result is that we can generate 18 million elements per second as opposed to 14 million per second in our earlier work. PDR.PODM scales well on uniform refinement cases running on DSM supercomputers.

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In earlier work, we proposed a Telescopic approach which is a multi-layered approach for extreme-scale parallel mesh generation and adaptation. In this paper, we describe the Parallel Data Refinement (PDR) layer of the Telescopic approach. Namely focus on PDR's: (i) design and implementation and (ii) evaluation using TetGen, an open source mesh generation software, on shared memory machines. We outline lessons learned and future directions for revisiting the PDR layer and making adjustments in the implementation of the remaining layers of the Telescopic approach. Figure 1 Telescopic Approach to parallel mesh generation and adaptation and PDR layer in the middle.