Stochastic rank correlation: a robust merit function for 2D/3D registration of image data obtained at different energies.
Stochastic rank correlation: a robust merit function for 2D/3D registration of image data obtained at different energies.
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DOI：
10.1118/1.3157111
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发表时间：
2009-08
期刊：
影响因子：
3.8
通讯作者：
Bergmann H
中科院分区：
文献类型：
--
作者：
Birkfellner W;Stock M;Figl M;Gendrin C;Hummel J;Dong S;Kettenbach J;Georg D;Bergmann H
在这篇文章中，作者评估了一种用于2D/3D配准的价值函数，称为随机等级相关(SRC)。SRC的特点是图像强度的差异不会影响配准结果，因此它结合了互相关(CC)型价值函数的数值优势和互信息型价值函数的灵活性。其基本思想是在图像的随机子集上实现配准，这允许有效地计算Spearman的等级相关系数。这种测量方法本质上不受比较图像中单调强度变换的影响，这使得它成为图像引导放射治疗中在不同能量水平下采集的模式内图像的理想解决方案。使用身体脊柱的2D/3D配准参考图像数据集进行初步评估。即使没有辐射定标，SRC也显示出与CC相比在稳健性和稳定性方面的显著改进。模式强度是另一种用于比较的评价函数，由于其收敛范围有限，结果相当差。图像含量为5%的SRC所需的时间与其他价值函数相比更好；增加图像含量不会显著影响算法的精度。作者总结说，SRC是IGRT中2D/3D配准和图像引导治疗的一种有前途的措施。
In this article, the authors evaluate a merit function for 2D/3D registration called stochastic rank correlation (SRC). SRC is characterized by the fact that differences in image intensity do not influence the registration result; it therefore combines the numerical advantages of cross correlation (CC)-type merit functions with the flexibility of mutual-information-type merit functions. The basic idea is that registration is achieved on a random subset of the image, which allows for an efficient computation of Spearman’s rank correlation coefficient. This measure is, by nature, invariant to monotonic intensity transforms in the images under comparison, which renders it an ideal solution for intramodal images acquired at different energy levels as encountered in intrafractional kV imaging in image-guided radiotherapy. Initial evaluation was undertaken using a 2D/3D registration reference image dataset of a cadaver spine. Even with no radiometric calibration, SRC shows a significant improvement in robustness and stability compared to CC. Pattern intensity, another merit function that was evaluated for comparison, gave rather poor results due to its limited convergence range. The time required for SRC with 5% image content compares well to the other merit functions; increasing the image content does not significantly influence the algorithm accuracy. The authors conclude that SRC is a promising measure for 2D/3D registration in IGRT and image-guided therapy in general.
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DOI：
10.1016/j.ijrobp.2006.10.026
发表时间：
2007-03-15
影响因子：
7
作者：
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通讯作者：
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影响因子：
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影响因子：
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影响因子：
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作者：
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通讯作者：
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影响因子：
3.8
作者：
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通讯作者：
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