PubMed Health⌕ Search

PubMed · 15046644

Virtual reality neurosurgery: a simulator blueprint.

Abstract

OBJECTIVE: This article details preliminary studies undertaken to integrate the most relevant advancements across multiple disciplines in an effort to construct a highly realistic neurosurgical simulator based on a distributed computer architecture. Techniques based on modified computational modeling paradigms incorporating finite element analysis are presented, as are current and projected efforts directed toward the implementation of a novel bidirectional haptic device. METHODS: Patient-specific data derived from noninvasive magnetic resonance imaging sequences are used to construct a computational model of the surgical region of interest. Magnetic resonance images of the brain may be coregistered with those obtained from magnetic resonance angiography, magnetic resonance venography, and diffusion tensor imaging to formulate models of varying anatomic complexity. RESULTS: The majority of the computational burden is encountered in the presimulation reduction of the computational model and allows realization of the required threshold rates for the accurate and realistic representation of real-time visual animations. CONCLUSION: Intracranial neurosurgical procedures offer an ideal testing site for the development of a totally immersive virtual reality surgical simulator when compared with the simulations required in other surgical subspecialties. The material properties of the brain as well as the typically small volumes of tissue exposed in the surgical field, coupled with techniques and strategies to minimize computational demands, provide unique opportunities for the development of such a simulator. Incorporation of real-time haptic and visual feedback is approached here and likely will be accomplished soon.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Mark A Spicer, Martin van Velsen, John P Caffrey, Michael L J Apuzzo. 2004. Virtual reality neurosurgery: a simulator blueprint.. https://doi.org/10.1227/01.neu.0000114139.16118.f2

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Generating correlated data for omics simulation.

Simulation of realistic omics data is a key input for benchmarking studies that help users obtain optimal computational pipelines. Omics data involves large numbers of measured features on each sample and these measures are generally correlated with each other. However, simulation too often ignores these correlations, perhaps due to computational and statistical hurdles of doing so. To alleviate this, we describe three approaches for generating omics-scale data with correlated measures which mimic real datasets. These approaches are all based on a Gaussian copula approach with a covariance matrix that decomposes into a diagonal part and a low-rank part. This decomposition allows for extremely efficient simulation, overcoming a hurdle for adoption of past methods. We use these approaches to demonstrate the importance of including correlation in two benchmarking applications. First, we show that variance of results from the popular DESeq2 method increases when dependence is included. Second, we demonstrate that CYCLOPS, a method for inferring circadian time of collection from transcriptomics, improves in performance when given gene-gene dependencies in some circumstances. We provide an R package, dependentsimr, that has efficient implementations of these methods and can generate dependent data with arbitrary marginal distributions, including discrete (binary, ordered categorical, Poisson, negative binomial), continuous (normal), or with an empirical distribution.

Computer Simulation↗

Addressing current challenges in cancer immunotherapy with mathematical and computational modelling.

The goal of cancer immunotherapy is to boost a patient's immune response to a tumour. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumour microenvironment, immune-modulating effects of conventional treatments and therapy-related toxicities. These complexities can be incorporated into mathematical and computational models of cancer immunotherapy that can then be used to aid in rational therapy design. In this review, we survey modelling approaches under the umbrella of the major challenges facing immunotherapy development, which encompass tumour classification, optimal treatment scheduling and combination therapy design. Although overlapping, each challenge has presented unique opportunities for modellers to make contributions using analytical and numerical analysis of model outcomes, as well as optimization algorithms. We discuss several examples of models that have grown in complexity as more biological information has become available, showcasing how model development is a dynamic process interlinked with the rapid advances in tumour-immune biology. We conclude the review with recommendations for modellers both with respect to methodology and biological direction that might help keep modellers at the forefront of cancer immunotherapy development.

Computer Simulation↗

Degradation of supercoiled plasmid DNA within a capillary device.

Supercoiled plasmid DNA is susceptible to fluid stress in large-scale manufacturing processes. A capillary device was used to generate controlled shear conditions and the effects of different stresses on plasmid DNA structure were investigated. Computational fluid dynamics (CFD) analysis was employed to characterize the flow environment in the capillary device and different analytical techniques were used to quantify the DNA breakage. It was found that the degradation of plasmid DNA occurred at the entrance of the capillary and that the shear stress within the capillary did not affect the DNA structure. The degradation rate of plasmids was well correlated with the average elongational strain rate or the pressure drop at the entrance region. The conclusion may also be drawn that laminar shear stress does not play a significant role in plasmid DNA degradation.

Computer Simulation↗