GUCCI - 基于血流数据的心脏队列研究的指导。
GUCCI - Guided Cardiac Cohort Investigation of Blood Flow Data.
出版信息
IEEE Trans Vis Comput Graph. 2023 Mar;29(3):1876-1892. doi: 10.1109/TVCG.2021.3134083. Epub 2023 Jan 30.
We present the framework GUCCI (Guided Cardiac Cohort Investigation), which provides a guided visual analytics workflow to analyze cohort-based measured blood flow data in the aorta. In the past, many specialized techniques have been developed for the visual exploration of such data sets for a better understanding of the influence of morphological and hemodynamic conditions on cardiovascular diseases. However, there is a lack of dedicated techniques that allow visual comparison of multiple data sets and defined cohorts, which is essential to characterize pathologies. GUCCI offers visual analytics techniques and novel visualization methods to guide the user through the comparison of predefined cohorts, such as healthy volunteers and patients with a pathologically altered aorta. The combination of overview and glyph-based depictions together with statistical cohort-specific information allows investigating differences and similarities of the time-dependent data. Our framework was evaluated in a qualitative user study with three radiologists specialized in cardiac imaging and two experts in medical blood flow visualization. They were able to discover cohort-specific characteristics, which supports the derivation of standard values as well as the assessment of pathology-related severity and the need for treatment.
我们提出了框架 GUCCI(引导式心脏队列研究),它提供了一个引导式可视化分析工作流程,用于分析主动脉中基于队列的测量血流数据。过去,已经开发了许多专门的技术来可视化探索这些数据集,以更好地理解形态和血流动力学条件对心血管疾病的影响。然而,缺乏专门的技术来允许对多个数据集和定义的队列进行可视化比较,这对于表征病理学至关重要。GUCCI 提供了可视化分析技术和新的可视化方法,指导用户比较预定义的队列,例如健康志愿者和主动脉病理学改变的患者。概述和基于字形的描述的组合以及统计队列特定信息允许调查时间相关数据的差异和相似性。我们的框架在一项定性用户研究中进行了评估,该研究涉及三位专门从事心脏成像的放射科医生和两位医学血流可视化专家。他们能够发现队列特异性特征,这支持了标准值的推导以及与病理学相关的严重程度和治疗需求的评估。