Spatial Conditioning of Transfer Functions Using Local Material Distributions
Spatial Conditioning of Transfer Functions Using Local Material Distributions
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DOI：
10.1109/tvcg.2010.195
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发表时间：
2010-11-01
影响因子：
5.2
通讯作者：
Ynnerman, Anders
中科院分区：
文献类型：
--
作者：
Lindholm, Stefan;Ljung, Patric;Ynnerman, Anders
在直接体绘制(DVR)的许多应用中，某种材料或特征的重要性高度依赖于它的相对空间位置。例如，在医疗诊断过程中，患者的症状往往会导致指定特定的特征、组织和器官。一个这样的例子是气囊，如果在体内不正常的位置发现它们，它是诊断可视化的关键部分。提出了一种基于用户指定材料依赖关系的空间局部化增强DVR传递函数设计的方法。语义表达用于根据不同材料之间的关系来定义条件，例如只有在靠近肝脏时才会导致碘摄取。基本的方法依赖于对材料分布的估计，而材料分布的估计是通过对数据的局部邻域与材料似然函数的近似进行加权而获得的。该信息被编码并用于根据用户的规范影响渲染。其结果是，通过允许用户抑制空间上不太重要的数据，改进了对重要特征的关注。与实际DVR实践的要求一致，该方法不需要显式的材料分割，而在大多数真实情况下是不可能实现的，或者是非常耗时的。该方案很好地扩展到更高的维度，这解释了多维传递函数和多变量数据。双能量计算机断层扫描是放射学中一种重要的新方法，它被用来证明这种可扩展性。在几个例子中，我们显示了对渲染图像中临床重要方面的显着改进。
In many applications of Direct Volume Rendering (DVR) the importance of a certain material or feature is highly dependent on its relative spatial location. For instance, in the medical diagnostic procedure, the patient's symptoms often lead to specification of features, tissues and organs of particular interest. One such example is pockets of gas which, if found inside the body at abnormal locations, are a crucial part of a diagnostic visualization. This paper presents an approach that enhances DVR transfer function design with spatial localization based on user specified material dependencies. Semantic expressions are used to define conditions based on relations between different materials, such as only render iodine uptake when close to liver. The underlying methods rely on estimations of material distributions which are acquired by weighing local neighborhoods of the data against approximations of material likelihood functions. This information is encoded and used to influence rendering according to the user's specifications. The result is improved focus on important features by allowing the user to suppress spatially less-important data. In line with requirements from actual clinical DVR practice, the methods do not require explicit material segmentation that would be impossible or prohibitively time-consuming to achieve in most real cases. The scheme scales well to higher dimensions which accounts for multi-dimensional transfer functions and multivariate data. Dual-Energy Computed Tomography, an important new modality in radiology, is used to demonstrate this scalability. In several examples we show significantly improved focus on clinically important aspects in the rendered images.