Nikos Chrisochoides

Non-rigid alignment of pre-operative MRI, fMRI, and DT-MRI with intra-operative MRI for enhanced visualization and navigation in image-guided neurosurgery.

OBJECTIVE The usefulness of neurosurgical navigation with current visualizations is seriously compromised by brain shift, which inevitably occurs during the course of the operation, significantly degrading the precise alignment between the pre-operative MR data and the intra-operative shape of the brain. Our objectives were (i) to evaluate the feasibility of non-rigid registration that compensates for the brain deformations within the time constraints imposed by neurosurgery, and (ii) to create augmented reality visualizations of critical structural and functional brain regions during neurosurgery using pre-operatively acquired fMRI and DT-MRI. MATERIALS AND METHODS Eleven consecutive patients with supratentorial gliomas were included in our study. All underwent surgery at our intra-operative MR imaging-guided therapy facility and have tumors in eloquent brain areas (e.g. precentral gyrus and cortico-spinal tract). Functional MRI and DT-MRI, together with MPRAGE and T2w structural MRI were acquired at 3 T prior to surgery. SPGR and T2w images were acquired with a 0.5 T magnet during each procedure. Quantitative assessment of the alignment accuracy was carried out and compared with current state-of-the-art systems based only on rigid registration. RESULTS Alignment between pre-operative and intra-operative datasets was successfully carried out during surgery for all patients. Overall, the mean residual displacement remaining after non-rigid registration was 1.82 mm. There is a statistically significant improvement in alignment accuracy utilizing our non-rigid registration in comparison to the currently used technology (p<0.001). CONCLUSIONS We were able to achieve intra-operative rigid and non-rigid registration of (1) pre-operative structural MRI with intra-operative T1w MRI; (2) pre-operative fMRI with intra-operative T1w MRI, and (3) pre-operative DT-MRI with intra-operative T1w MRI. The registration algorithms as implemented were sufficiently robust and rapid to meet the hard real-time constraints of intra-operative surgical decision making. The validation experiments demonstrate that we can accurately compensate for the deformation of the brain and thus can construct an augmented reality visualization to aid the surgeon.

A load balancing framework for adaptive and asynchronous applications

We describe the design of a flexible load balancing framework and runtime software system for supporting the development of adaptive applications on distributed-memory parallel computers. The runtime system supports a global namespace, transparent object migration, automatic message forwarding and routing, and automatic load balancing. These features can be used at the discretion of the application developer in order to simplify program development and to eliminate complex bookkeeping associated with mobile data objects. An evaluation of this system in the context of a three-dimensional tetrahedral advancing front parallel mesh generator shows that overall runtime improvements of 15 percent compared to common stop-and-repartition load balancing methods, 30 percent compared to explicit intrusive load balancing methods, and 42 percent compared to no load balancing are possible on large processor configurations. At the same time, the overheads attributable to the runtime system are a fraction of 1 percent of the total runtime. The parallel advancing front method is a coarse-grained and highly adaptive application and therefore exercises all of the features of the runtime system.

Guaranteed: quality parallel delaunay refinement for restricted polyhedral domains

We describe a distributed memory parallel Delaunay refinement algorithm for polyhedral domains which can generate meshes containing tetrahedra with circumradius to shortest edge ratio less than 2, as long as the angle separating any two incident segments and/or facets is between 90° and 270° degrees. Input to our implementation is an element--wise partitioned, conforming Delaunay mesh of a restricted polyhedral domain which has been distributed to the processors of a parallel system. The submeshes of the distributed mesh are then independently refined by concurrently inserting new mesh vertices.Our algorithm allows a new mesh vertex to affect both the submesh tetrahedralizations and the submesh interfaces induced by the partitioning. This flexibility is crucial to ensure mesh quality, but it introduces unpredictable and variable latencies due to long delays in gathering remote data required for updating mesh data structures. In our experiments, more than 80% of this latency was masked with computation due to the fine--grained concurrency of our algorithm.Our experiments also show that the algorithm is efficient in practice, even for certain domains whose boundaries do not conform to the theoretical limits imposed by the algorithm. The algorithm we describe is the first step in the development of much more sophisticated guaranteed--quality parallel mesh generation algorithms.

Modeling class cohesion as mixtures of latent topics

The paper proposes a new measure for the cohesion of classes in Object-Oriented software systems. It is based on the analysis of latent topics embedded in comments and identifiers in source code. The measure, named as Maximal Weighted Entropy, utilizes the Latent Dirichlet Allocation technique and information entropy measures to quantitatively evaluate the cohesion of classes in software. This paper presents the principles and the technology that stand behind the proposed measure. Two case studies on a large open source software system are presented. They compare the new measure with an extensive set of existing metrics and use them to construct models that predict software faults. The case studies indicate that the novel measure captures different aspects of class cohesion compared to the existing cohesion measures and improves fault prediction for most metrics, which are combined with Maximal Weighted Entropy.

Parallel unstructured mesh generation by an advancing front method

Mesh generation is a critical step in high fidelity computational simulations. High-quality and high-density meshes are required to accurately capture the complex physical phenomena. A robust approach for a parallel framework has been developed to generate large-scale meshes in a short period of time. A coarse tetrahedral mesh is generated first to provide the basis of block interfaces and then is partitioned into a number of sub-domains using METIS partitioning algorithms. A volume mesh is generated on each sub-domain in parallel using an advancing front method. Dynamic load balancing is achieved by evenly distributing work among the processors. All the sub-domains are combined to create a single volume mesh. The combined volume mesh can be smoothed to remove the artifacts in the interfaces between sub-domains. A void region is defined inside each sub-domain to reduce the data points during the smoothing operation. The scalability of the parallel mesh generation is evaluated to quantify the improvement on shared- and distributed-memory computer systems.

Toward real-time image guided neurosurgery using distributed and grid computing

Neurosurgical resection is a therapeutic intervention in the treatment of brain tumors. Precision of the resection can be improved by utilizing magnetic resonance imaging (MRI) as an aid in decision making during image guided neurosurgery (IGNS). Image registration adjusts pre-operative data according to intra-operative tissue deformation. Some of the approaches increase the registration accuracy by tracking image landmarks through the whole brain volume. High computational cost used to render these techniques inappropriate for clinical applications. In this paper we present a parallel implementation of a state of the art registration method, and a number of needed incremental improvements. Overall, we reduced the response time for registration of an average dataset from about an hour and for some cases more than an hour to less than seven minutes, which is within the time constraints imposed by neurosurgeons. For the first time in clinical practice we demonstrated, that with the help of distributed computing non-rigid MRI registration based on volume tracking can be computed intra-operatively

An Evaluation of a Framework for the Dynamic Load Balancing of Highly Adaptive and Irregular Parallel Applications

We present an evaluation of a flexible framework and runtime software system for the dynamic load balancing of asynchronous and highly adaptive and irregular applications. These applications, which include parallel unstructured and adaptive mesh refinement, serve as building blocks for a large class of scientific applications. Extensive study has lead to the development of solutions to the dynamic load balancing problem for loosely synchronous and computation intensive programs; however, these methods are not suitable for asynchronous and highly adaptive applications. We evaluate a new software framework which includes support for an Active Messages style communication mechanism, global name space, transparent object migration, and preemptive decision making. Our results from both a 3-dimensional parallel advancing front mesh generation program, as well as a synthetic microbenchmark, indicate that this new framework out-performs two existing general-purpose, well-known, and widely used software systems for the dynamic load balancing of adpative and irregular parallel applications.

Practical and efficient point insertion scheduling method for parallel guaranteed quality delaunay refinement

We describe a parallel scheduler, for guaranteed quality parallel mesh generation and refinement methods. We prove a sufficient condition for the new points to be independent, which permits the concurrent insertion of more than two points without destroying the conformity and Delaunay properties of the mesh. The scheduling technique we present is much more efficient than existing coloring methods and thus it is suitable for practical use. The condition for concurrent point insertion is based on the comparison of the distance between the candidate points against the upper bound on triangle circumradius in the mesh. Our experimental data show that the scheduler introduces a small overhead (in the order of 1--2% of the total execution time) it requires local and structured communication compared to irregular, variable and unpredictable communication of the other existing practical parallel guaranteed quality mesh generation and refinement method. Finally, on a cluster of more than 100 workstations using a simple (block) decomposition our data show that we can generate about 900 million elements in less than 300 seconds.

Three-dimensional delaunay refinement for multi-core processors

We develop the first ever fully functional three-dimensional guaranteed quality parallel graded Delaunay mesh generator. First, we prove a criterion and a sufficient condition of Delaunay-independence of Steiner points in three dimensions. Based on these results, we decompose the iteration space of the sequential Delaunay refinement algorithm by selecting independent subsets from the set of the candidate Steiner points without resorting to rollbacks. We use an octree which overlaps the mesh for a coarse-grained decomposition of the set of candidate Steiner points based on their location. We partition the worklist containing poor quality tetrahedra into independent lists associated with specific separated leaves of the octree. Finally, we describe an example parallel implementation using a publicly available state-of-the art sequential Delaunay library (Tetgen). This work provides a case study for the design of abstractions and parallel frameworks for the use of complex labor intensive sequential codes on multicore architectures.

Simultaneous mesh generation and partitioning for Delaunay meshes

In this paper, we present a new approach for the parallel generation and partitioning of unstructured 3D Delaunay meshes. The new approach couples the mesh generation and partitioning problems into a single optimization problem. Traditionally, these two problems are solved separately, first generating the mesh (usually sequentially) and then partitioning the mesh, either sequentially or in parallel. In the traditional approach, the overheads due to I/O and data movement exceed 50% of the total execution time. Even if parallel partitioning schemes are employed, data movement, synchronization, and data structure translation overheads are high; for applications which require frequent remeshing (e.g. crack growth simulations), these overheads are prohibitive. We present a method for solving the mesh partitioning and placement problem simultaneously with the mesh generation problem. By eliminating unnecessary and redundant cache, local, and remote memory accesses, we can speed up the generation and redistribution process, for very large meshes, by almost an order of magnitude compared to traditional approaches. Our results show that we can achieve nearly perfect equi-distribution of mesh elements over the processors, while maintaining reasonably good separator size, all while improving the quality of the mesh by eliminating many of the problems inherent in traditional parallel constrained mesh generation.