Uncluttered single-image visualization of the abdominal aortic vessel tree: method and evaluation.
Uncluttered single-image visualization of the abdominal aortic vessel tree: method and evaluation.
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腹主动脉血管树的整洁单图像可视化：方法和评估。
DOI：
10.1118/1.3243866
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发表时间：
2009
期刊：
影响因子：
3.8
通讯作者：
Napel,Sandy
中科院分区：
文献类型：
--
作者：
Won,Joong-Ho;Rosenberg,Jarrett;Rubin,GeoffreyD;Napel,Sandy
目的提出一种将CT或MR血管成像获得的腹主动脉及其分支在单一的2D风格图像上显示的方法。方法将腹主动脉血管建模为一个关节对象，其底层拓扑结构是一个根树。该算法的输入是腹主动脉及其分支的3D中心线及其相关的直径信息。可视化问题被表述为找到中心线的边界框的空间配置的优化问题，该中心线的边界框最类似于输入向给定观察方向(例如，前后方向)的投影，而不引入框之间的交集。该优化算法最小化关于边界框的重叠和与输入的偏差的得分函数。该算法的输出用于在平面上产生由关联直径信息调制的2D中心线构成的风格可视化。作者进行了初步评估，要求三名放射科医生从5个病例的30个可视化图像中标记366个动脉分支。五名患者中的每一位都被呈现在六种不同的不同图像中，这些图像是从十种不同的图像中选出的，其中三个最低得分，三个最高得分。对于每个标签，他们都分配了置信度和失真评级(低/中/高)。他们研究了可视化测量的量化指标与放射科医生的主观评分之间的关联。结果所有的可视化结果都没有分支重叠。三种阅读器的标记准确率分别为93.4%、94.5%和95.4%。在1098个样本中，失真率为低：77.39%，中：10.48%，高：12.12%。置信度低：5.56%，中：16.50%，高：77.94%。关联研究表明，所提出的量化指标可以预测读者的主观评分，并建议读者选择得分最低的可视化方法。结论腹主动脉树二维投影中消除误导性虚假相交的方法保留了整体形状，且不影响分支的准确识别。
PurposeThe authors develop a method to visualize the abdominal aorta and its branches, obtained by CT or MR angiography, in a single 2D stylistic image without overlap among branches.MethodsThe abdominal aortic vasculature is modeled as an articulated object whose underlying topology is a rooted tree. The inputs to the algorithm are the 3D centerlines of the abdominal aorta, its branches, and their associated diameter information. The visualization problem is formulated as an optimization problem that finds a spatial configuration of the bounding boxes of the centerlines most similar to the projection of the input into a given viewing direction (e.g., anteroposterior), while not introducing intersections among the boxes. The optimization algorithm minimizes a score function regarding the overlap of the bounding boxes and the deviation from the input. The output of the algorithm is used to produce a stylistic visualization, made of the 2D centerlines modulated by the associated diameter information, on a plane. The authors performed a preliminary evaluation by asking three radiologists to label 366 arterial branches from the 30 visualizations of five cases produced by the method. Each of the five patients was presented in six different variant images, selected from ten variants with the three lowest and three highest scores. For each label, they assigned confidence and distortion ratings (low/medium/high). They studied the association between the quantitative metrics measured from the visualization and the subjective ratings by the radiologists.ResultsAll resulting visualizations were free from branch overlaps. Labeling accuracies of the three readers were 93.4%, 94.5%, and 95.4%, respectively. For the total of 1098 samples, the distortion ratings were low: 77.39%, medium: 10.48%, and high: 12.12%. The confidence ratings were low: 5.56%, medium: 16.50%, and high: 77.94%. The association study shows that the proposed quantitative metrics can predict a reader's subjective ratings and suggests that the visualization with the lowest score should be selected for readers.ConclusionsThe method for eliminating misleading false intersections in 2D projections of the abdominal aortic tree conserves the overall shape and does not diminish accurate identifiability of the branches.
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DOI：
--
发表时间：
2003
期刊：
IEEE Visualization, 2003. VIS 2003.
影响因子：
--
作者：
Laurent Saroul;Sebastian Gerlach;R. Hersch
通讯作者：
R. Hersch
影响因子：
3.8
作者：
Paik, DS;Beaulieu, CF;Napel, S
通讯作者：
Napel, S
DOI：
10.1023/b:visi.0000042934.15159.49
发表时间：
2005-01-01
影响因子：
19.5
作者：
Felzenszwalb, PF;Huttenlocher, DP
通讯作者：
Huttenlocher, DP
从体积数据自动生成曲面重组：方法和评估。
影响因子：
19.7
作者：
Raman,Raghav;Napel,Sandy;Beaulieu,ChristopherF;Bain,EricS;JeffreyJr,RBrooke;Rubin,GeoffreyD
通讯作者：
Rubin,GeoffreyD
使用半刚性模型登记肺结节：方法和初步结果。
影响因子：
3.8
作者：
Sun,Shaohua;Rubin,GeoffreyD;Paik,David;Steiner,RobertM;Zhuge,Feng;Napel,Sandy
通讯作者：
Napel,Sandy