Oeltze Steffen, Doleisch Helmut, Hauser Helwig, Muigg Philipp, Preim Bernhard
Department of Simulation and Graphics, University of Magdeburg, Germany.
IEEE Trans Vis Comput Graph. 2007 Nov-Dec;13(6):1392-9. doi: 10.1109/TVCG.2007.70569.
Perfusion data are dynamic medical image data which characterize the regional blood flow in human tissue. These data bear a great potential in medical diagnosis, since diseases can be better distinguished and detected at an earlier stage compared to static image data. The wide-spread use of perfusion data is hampered by the lack of efficient evaluation methods. For each voxel, a time-intensity curve characterizes the enhancement of a contrast agent. Parameters derived from these curves characterize the perfusion and have to be integrated for diagnosis. The diagnostic evaluation of this multi-field data is challenging and time-consuming due to its complexity. For the visual analysis of such datasets, feature-based approaches allow to reduce the amount of data and direct the user to suspicious areas. We present an interactive visual analysis approach for the evaluation of perfusion data. For this purpose, we integrate statistical methods and interactive feature specification. Correlation analysis and Principal Component Analysis (PCA) are applied for dimensionreduction and to achieve a better understanding of the inter-parameter relations. Multiple, linked views facilitate the definition of features by brushing multiple dimensions. The specification result is linked to all views establishing a focus+context style of visualization in 3D. We discuss our approach with respect to clinical datasets from the three major application areas: ischemic stroke diagnosis, breast tumor diagnosis, as well as the diagnosis of the coronary heart disease (CHD). It turns out that the significance of perfusion parameters strongly depends on the individual patient, scanning parameters, and data pre-processing.
灌注数据是动态医学图像数据，用于表征人体组织中的局部血流情况。这些数据在医学诊断中具有巨大潜力，因为与静态图像数据相比，疾病在早期阶段能够得到更好的区分和检测。然而，由于缺乏有效的评估方法，灌注数据的广泛应用受到了阻碍。对于每个体素，时间-强度曲线表征了造影剂的增强情况。从这些曲线中导出的参数表征了灌注情况，并且必须进行整合以用于诊断。由于这种多字段数据的复杂性，对其进行诊断评估具有挑战性且耗时。对于此类数据集的视觉分析，基于特征的方法可以减少数据量，并将用户引导至可疑区域。我们提出了一种用于评估灌注数据的交互式视觉分析方法。为此，我们整合了统计方法和交互式特征指定。应用相关分析和主成分分析（PCA）进行降维，并更好地理解参数间的关系。多个链接视图通过刷选多个维度来促进特征的定义。指定结果与所有视图相链接，在三维空间中建立了一种焦点+上下文的可视化风格。我们针对来自三个主要应用领域的临床数据集讨论了我们的方法：缺血性中风诊断、乳腺肿瘤诊断以及冠心病（CHD）诊断。结果表明，灌注参数的重要性在很大程度上取决于个体患者、扫描参数和数据预处理。
