Optimal setting of image bounding box can improve registration accuracy of diffusion tensor tractography.
Optimal setting of image bounding box can improve registration accuracy of diffusion tensor tractography.
复制标题
图像边界框的优化设置可以提高扩散张量纤维束成像的配准精度。
DOI：
10.1007/s11548-013-0934-3
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Saito N.
中科院分区：
文献类型：
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作者：
Yoshino M;Kin T;Saito T;Nakagawa D;Nakatomi H;Kunimatsu A;Oyama H;Saito N.
目的：当我们将弥散张量纤维束成像（DTT）与解剖图像（如采用稳态采集的快速成像（FIESTA））配准时，我们将B0图像配准到FIESTA。与刚性配准相比，使用非刚性配准可以将DTT B0图像精确配准到FIESTA，尽管非刚性方法缺乏便利性。我们报告的效果，图像数据的边界框设置上的配准精度使用归一化互信息（NMI）methodsMRI扫描的10例患者在这项研究中使用。在对照组中，在不修改边界框的情况下进行配准，并将结果与使用限制在感兴趣区域（ROI）的多个边界框重新配准的组进行比较。结果在FIESTA和B0图像共有的3个解剖特征点处，3个解剖特征点处配准后的未对准距离的平均值（SD）从tom，），tom，（，和tom，（，结论将图像数据边界框缩小到ROI可提高配准B0图像的准确性通过NMI方法将其转化为FIESTA。利用我们提出的方法，可以在非常简单的步骤中提高精度，并且该方法可能被证明对DTT与解剖图像的配准有用
PurposeWhen we register diffusion tensor tractography (DTT) to anatomical images such as fast imaging employing steady-state acquisition (FIESTA), we register the B0 image to FIESTA. Precise registration of the DTT B0 image to FIESTA is possible with non-rigid registration compared to rigid registration, although the non-rigid methods lack convenience. We report the effect of image data bounding box settings on registration accuracy using a normalized mutual information (NMI) methodMethodsMRI scans of 10 patients were used in this study. Registration was performed without modification of the bounding box in the control group, and the results were compared with groups re-registered using multiple bounding boxes limited to the region of interest (ROI). The distance of misalignment after registration at 3 anatomical characteristic points that are common to both FIESTA and B0 images was used as an index of accuracy.ResultsMean (SD) misalignment at the 3 anatomical points decreased significantly fromtomm,),tomm, (, andtomm, (, each showing improvement compared to the control groupConclusionNarrowing the image data bounding box to the ROI improves the accuracy of registering B0 images to FIESTA by NMI method. With our proposed methodology, accuracy can be improved in extremely easy steps, and this methodology may prove useful for DTT registration to anatomical image