Spatio-temporal data fusion for 3D+T image reconstruction in cerebral angiography.
Spatio-temporal data fusion for 3D+T image reconstruction in cerebral angiography.
复制标题
脑血管造影中 3D T 图像重建的时空数据融合。
DOI：
10.1109/tmi.2009.2039645
复制
发表时间：
2010
影响因子：
10.6
通讯作者：
Malek,AdelM
中科院分区：
文献类型：
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作者：
Copeland,AndrewD;Mangoubi,RamiS;Desai,MukundN;Mitter,SanjoyK;Malek,AdelM
本文提供了一种生成显示脑血流动力学的高分辨率三维图像时间序列的框架。这些序列有可能在医疗过程中允许图像反馈，从而促进对狭窄、动脉瘤和血块等病理异常的检测和观察。3D时间序列是通过将单个静态3D模型与同一成像区域的两个2D投影的时间序列融合而成的。融合过程使用变分方法，该方法将体积约束为既有由边缘分隔的平滑变化区域，又有非零支撑的稀疏区域。变分问题是利用血管造影术问题的时空结构的Gauss-Seidel算法的改进版本来解决的。3D时间序列结果使用等值面的时间序列、来自任意角度或姿势的合成X射线、以及使用颜色编码显示对比的血液前锋的到达时间的3D表面来可视化。衍生的可视化为医生提供了以前无法获得的丰富信息，这些信息可以导致更安全的手术，包括更快地定位血流改变异常，如血栓，以及较低的程序性X射线暴露。文中还给出了该算法在计算模型数据上的定量信噪比和其他性能分析。
This paper provides a framework for generating high resolution time sequences of 3D images that show the dynamics of cerebral blood flow. These sequences have the potential to allow image feedback during medical procedures that facilitate the detection and observation of pathological abnormalities such as stenoses, aneurysms, and blood clots. The 3D time series is constructed by fusing a single static 3D model with two time sequences of 2D projections of the same imaged region. The fusion process utilizes a variational approach that constrains the volumes to have both smoothly varying regions separated by edges and sparse regions of nonzero support. The variational problem is solved using a modified version of the Gauss–Seidel algorithm that exploits the spatio-temporal structure of the angiography problem. The 3D time series results are visualized using time series of isosurfaces, synthetic X-rays from arbitrary perspectives or poses, and 3D surfaces that show arrival times of the contrasted blood front using color coding. The derived visualizations provide physicians with a previously unavailable wealth of information that can lead to safer procedures, including quicker localization of flow altering abnormalities such as blood clots, and lower procedural X-ray exposure. Quantitative SNR and other performance analysis of the algorithm on computational phantom data are also presented.