Computer Graphics Tools for Understanding Tensor-Valued Volume Data: A Painting Metaphor

这个多学科的研究项目将调查，应用和评估新的计算机图形工具来理解矢量和二阶张量值体数据。 这些工具的有效性将通过它们对几种特定物理现象的洞察程度进行评估：完全发展的湍流，多发性硬化症小鼠类似物的疾病进展以及小鼠大脑的新生儿发育。 新技术的应用和与其他领域研究人员的合作提供了验证技术的机会，并确保它们在解决科学问题时能够做出反应。 这些技术将使用绘画隐喻来开发。 几个世纪以来，艺术家们一直采用分而治之的方法来创作绘画，用不同的颜料层来表现场景的不同方面。 此外，艺术家使用具有纹理，大小，形状，颜色和不透明度等许多特征的笔触在画布上表现场景的重要特征，描绘场景的本质并消除不必要的细节。 这两种技术，以及使用它们的集体艺术经验，都可以应用于显示体数据的计算机图形学。 用于理解向量值和张量值体数据的技术特别受益，因为体包含如此多的信息，并且因为张量和向量值具有许多相关的、相互依赖的方面。 现有技术在有效地提取数据和同时显示数据的许多相关方面的能力方面是有限的，但新技术将解决这两个问题。 因此，它们将更有效地产生对所测量的物理现象的洞察力，并传达这种洞察力。 由于这些技术将应用于两个学科中的三个具体问题，因此它们可能具有更广泛的应用。

This multi-disciplinary research project will investigate, apply, and evaluate new computer graphics tools for understanding vector- and second-order tensor-valued volume data. The efficacy of the tools will be evaluated by the extent to which they generate insight into several specific physical phenomena: fully-developed turbulent flow, the progression of disease in a mouse analogue of multiple sclerosis, and the neonatal development of the mouse brain. The application of the new techniques and collaboration with researchers in other fields provides the opportunity to validate the techniques and ensure that they are responsive in addressing the scientific problems. The techniques will be developed using a painting metaphor. For centuries artists have used a divide-and-conquer approach to creating paintings, representing different aspects of a scene in different layers of paint. In addition, artists use brush strokes, with their many characteristics of texture, size, shape, color, and opacity, to represent important features of a scene on a canvas, depicting the essence of the scene and eliminating unnecessary detail. Both techniques, and the collective artistic experience in using them, can be applied to computer graphics in the context of displaying volume data. Techniques for understanding vector-valued and tensor-valued volume data stand to benefit particularly, because volumes contain so much information and because tensor and vector values have many relevant, interdependent aspects. Existing techniques are limited in their ability to abstract the data effectively and to display the many relevant aspects of the data simultaneously, but the new techniques will address both of these problems. As a result, they will be more effective in generating insight into the physical phenomena measured as well as in communicating the insight. Because the techniques will be developed with application to three specific problems in two disciplines, they are likely t o have broader application.