McConell Brain Imaging Center at the Montreal Neurological Institute (MNI), 3801 University St, Montre´al, QC H3A 2B4, Canada.
IEEE Trans Vis Comput Graph. 2012 Feb;18(2):332-52. doi: 10.1109/TVCG.2011.50.
Mixed reality visualizations are increasingly studied for use in image guided surgery (IGS) systems, yet few mixed reality systems have been introduced for daily use into the operating room (OR). This may be the result of several factors: the systems are developed from a technical perspective, are rarely evaluated in the field, and/or lack consideration of the end user and the constraints of the OR. We introduce the Data, Visualization processing, View (DVV) taxonomy which defines each of the major components required to implement a mixed reality IGS system. We propose that these components be considered and used as validation criteria for introducing a mixed reality IGS system into the OR. A taxonomy of IGS visualization systems is a step toward developing a common language that will help developers and end users discuss and understand the constituents of a mixed reality visualization system, facilitating a greater presence of future systems in the OR. We evaluate the DVV taxonomy based on its goodness of fit and completeness. We demonstrate the utility of the DVV taxonomy by classifying 17 state-of-the-art research papers in the domain of mixed reality visualization IGS systems. Our classification shows that few IGS visualization systems' components have been validated and even fewer are evaluated.
混合现实可视化技术越来越多地被应用于图像引导手术（IGS）系统中，但很少有混合现实系统被引入手术室（OR）进行日常使用。这可能是由于以下几个因素造成的：系统是从技术角度开发的，很少在现场进行评估，并且/或者没有考虑最终用户和 OR 的限制。我们引入了数据、可视化处理、视图（DVV）分类法，该分类法定义了实现混合现实 IGS 系统所需的主要组件。我们建议将这些组件视为引入混合现实 IGS 系统到 OR 的验证标准。IGS 可视化系统的分类法是朝着开发通用语言迈出的一步，这将有助于开发人员和最终用户讨论和理解混合现实可视化系统的组成部分，从而使未来的系统在 OR 中得到更广泛的应用。我们基于其拟合优度和完整性来评估 DVV 分类法。我们通过对 17 篇关于混合现实可视化 IGS 系统的最新研究论文进行分类，展示了 DVV 分类法的实用性。我们的分类表明，很少有 IGS 可视化系统的组件得到了验证，甚至更少的组件得到了评估。
