Department for Computer Science, University of Leipzig, PF 100920, 04009 Leipzig, Germany.
IEEE Trans Vis Comput Graph. 2013 Jun;19(6):900-12. doi: 10.1109/TVCG.2012.318.
Four-dimensional MRI is an in vivo flow imaging modality that is expected to significantly enhance the understanding of cardiovascular diseases. Among other fields, 4D MRI provides valuable data for the research of cardiac blood flow and with that the development, diagnosis, and treatment of various cardiac pathologies. However, to gain insights from larger research studies or to apply 4D MRI in the clinical routine later on, analysis techniques become necessary that allow to robustly identify important flow characteristics without demanding too much time and expert knowledge. Heart muscle contractions and the particular complexity of the flow in the heart imply further challenges when analyzing cardiac blood flow. Working toward the goal of simplifying the analysis of 4D MRI heart data, we present a visual analysis method using line predicates. With line predicates precalculated integral lines are sorted into bundles with similar flow properties, such as velocity, vorticity, or flow paths. The user can combine the line predicates flexibly and by that carve out interesting flow features helping to gain overview. We applied our analysis technique to 4D MRI data of healthy and pathological hearts and present several flow aspects that could not be shown with current methods. Three 4D MRI experts gave feedback and confirmed the additional benefit of our method for their understanding of cardiac blood flow.
四维磁共振成像是一种活体血流成像方式，有望极大地提高对心血管疾病的认识。在其他领域，4D MRI 为心脏血流研究提供了有价值的数据，并为各种心脏病理的发展、诊断和治疗提供了依据。然而，为了从更大的研究中获得深入的见解，或者以后在临床常规中应用 4D MRI，就需要有分析技术来稳健地识别重要的血流特征，而不需要太多的时间和专业知识。心肌收缩和心脏内血流的特殊复杂性在分析心脏血流时带来了进一步的挑战。为了简化 4D MRI 心脏数据分析的目标，我们提出了一种使用线谓词的可视化分析方法。通过预先计算线谓词，将积分线按相似的流动特性（如速度、涡度或流动路径）分类成束。用户可以灵活地组合线谓词，从而提取出有趣的流动特征，帮助获得整体概览。我们将我们的分析技术应用于健康和病态心脏的 4D MRI 数据，并展示了一些当前方法无法显示的流动特征。三位 4D MRI 专家提供了反馈，并确认了我们的方法对他们理解心脏血流的额外帮助。
