Andriy Kot

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.

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

Towards Exascale Parallel Delaunay Mesh Generation

Mesh generation is a critical component for many (bio-)engineering applications. However, parallel mesh generation codes, which are essential for these applications to take the fullest advantage of the high-end computing platforms, belong to the broader class of adaptive and irregular problems, and are among the most complex, challenging, and labor intensive to develop and maintain. As a result, parallel mesh generation is one of the last applications to be installed on new parallel architectures. In this paper we present a way to remedy this problem for new highly-scalable architectures. We present a multi-layered tetrahedral/triangular mesh generation approach capable of delivering and sustaining close to 1018 of concurrent work units. We achieve this by leveraging concurrency at different granularity levels using a hybrid algorithm, and by carefully matching these levels to the hierarchy of the hardware architecture. This paper makes two contributions: (1) a new evolutionary path for developing multi-layered parallel mesh generation codes capable of increasing the concurrency of the state-of-the-art parallel mesh generation methods by at least 10 orders of magnitude and (2) a new abstraction for multi-layered runtime systems that target parallel mesh generation codes, to efficiently orchestrate intra- and inter-layer data movement and load balancing for current and emerging multi-layered architectures with deep memory and network hierarchies.

Evaluation of Remote Memory Access Communication on the Cray XT3

This paper evaluates remote memory access (RMA) communication capabilities and performance on the Cray XT3. We discuss properties of the network hardware and portals networking software layer and corresponding implementation issues for SHMEM and ARMCI portable RMA interfaces. The performance of these interfaces is studied and compared to MPI performance.

An ITK implementation of a physics-based non-rigid registration method for brain deformation in image-guided neurosurgery

As part of the ITK v4 project efforts, we have developed ITK filters for physics-based non-rigid registration (PBNRR), which satisfies the following requirements: account for tissue properties in the registration, improve accuracy compared to rigid registration, and reduce execution time using GPU and multi-core accelerators. The implementation has three main components: (1) Feature Point Selection, (2) Block Matching (mapped to both multi-core and GPU processors), and (3) a Robust Finite Element Solver. The use of multi-core and GPU accelerators in ITK v4 provides substantial performance improvements. For example, for the non-rigid registration of brain MRIs, the performance of the block matching filter on average is about 10 times faster when 12 hyperthreaded multi-cores are used and about 83 times faster when the NVIDIA Tesla GPU is used in Dell Workstation.

Effective out-of-core parallel Delaunay mesh refinement using off-the-shelf software

We present two cost-effective and high-performance out-of-core parallel mesh generation algorithms and their implementation on cluster of workstations (CoWs). The total wall-clock time including wait-in-queue delays for the out-of-core methods on a small cluster (16 processors) is three times shorter than the total wall-clock time for the in-core generation of the same size mesh (about a billion elements) using 121 processors. Our best out-of-core method, for mesh sizes that fit completely in the core of the CoWs, is about 5% slower than its in-core parallel counterpart method. This is a modest performance penalty for savings of many hours in response time. Both the in-core and out-of-core methods use the best publicly available off-the-shelf sequential in-core Delaunay mesh generator.

The Evaluation of an Effective Out-of-Core Run-Time System in the Context of Parallel Mesh Generation

We present an out-of-core run-time system that supports effective parallel computation of large irregular and adaptive problems, in particular unstructured mesh generation (PUMG). PUMG is a highly challenging application due to intensive memory accesses, unpredictable communication patterns, and variable and irregular data dependencies reflecting the unstructured spatial connectivity of mesh elements. Our runtime system allows to transform the footprint of parallel applications from wide and shallow into narrow and deep by extending the memory utilization to the out-of-core level. It simplifies and streamlines the development of otherwise highly time consuming out-of-core applications as well as the converting of existing applications. It utilizes disk, network and memory hierarchy to achieve high utilization of computing resources without sacrificing performance with PUMG. The runtime system combines different programming paradigms: multi-threading within the nodes using industrial strength software framework, one-sided active messages among the nodes, and an out-of-core subsystem for managing large datasets. We performed an evaluation on traditional parallel platforms to stress test all layers of the run-time system using three different PUMG methods with significantly varying communication and synchronization patterns. We demonstrated high overlap in computation, communication, and disk I/O which results in good performance when computing large out-of-core problems. The runtime system adds very small overhead~(up to 18\% on most configurations) when computing in-core which means performance is not compromised.

Green multi-layered "smart" memory management system

We investigate the feasibility of using outdated machines with slow processors for tolerating disk latencies for computation and data intensive parallel adaptive and irregular applications

Grid-Enabled Software Environment for Enhanced Dynamic Data-Driven Visualization and Navigation During Image-Guided Neurosurgery

In this paper we present our experience with an Image Guided Neurosurgery Grid-enabled Software Environment (IGNS-GSE) which integrates real-time acquisition of intraoperative Magnetic Resonance Imaging (IMRI) with the preoperative MRI, fMRI, and DT-MRI data. We describe our distributed implementation of a non-rigid image registration method which can be executed over the Grid. Previously, non-rigid registration algorithms which use landmark tracking across the entire brain volume were considered not practical because of the high computational demands. The IGNS-GSE, for the first time ever in clinical practice, alleviated this restriction. We show that we can compute and present enhanced MR images to neurosurgeons during the tumor resection within minutes after IMRI acquisition. For the last 12 months this software system is used routinely (on average once a month) for clinical studies at Brigham and Womens Hospital in Boston, MA. Based on the analysis of the registration results, we also present future directions which will take advantage of the vast resources of the Grid to improve the accuracy of the method in places of the brain where precision is critical for the neurosurgeons.

Parallel Out-of-Core Constrained Delaunay Mesh Generation

In this paper we present two approaches for parallel out-of-core mesh generation. The first approach is based on a traditional prioritized page replacement algorithm using prioritized version of accepted LRU replacement scheme proposed by Salmon et al. for n-body calculations. The second approach is based on the percolation model proposed for the HTMT petaflops design. We evaluate both approaches using the parallel constrained Delaunay mesh generation method. Our preliminary data suggest that for problem sizes up to half a billion element meshes the traditional approach is very effective. However for larger problem sizes (in the order of billions of elements) the traditional approach becomes prohibitively expensive, but it appears from our preliminary data that the non-traditional percolation approach is a good alternative.