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Effects of Image Quality on the Fundamental Limits of Image Registration Accuracy

机译:图像质量对图像配准精度的基本限制的影响

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摘要

For image-guided procedures, the imaging task is often tied to the registration of intraoperative and preoperative images to a common coordinate system. While the accuracy of this registration is a vital factor in system performance, there is a relatively little work that relates registration accuracy to image quality factors, such as dose, noise, and spatial resolution. To create a theoretical model for such a relationship, we present a Fisher information approach to analyze registration performance in explicit dependence on the underlying image quality factors of image noise, spatial resolution, and signal power spectrum. The model yields analysis of the Cramer–Rao lower bound (CRLB), in registration accuracy as a function of factors governing image quality. Experiments were performed in simulation of computed tomography low-contrast soft tissue images and high-contrast bone (head and neck) images to compare the measured accuracy [root mean squared error (RMSE) of the estimated transformations] with the theoretical lower bound. Analysis of the CRLB reveals that registration performance is closely related to the signal-to-noise ratio of the cross-correlation space. While the lower bound is optimistic, it exhibits consistent trends with experimental findings and yields a method for comparing the performance of various registration methods and similarity metrics. Further analysis validated a method for determining optimal post-processing (image filtering) for registration. Two figures of merit (CRLB and RMSE) are presented that unify models of image quality with registration performance, providing an important guide to optimizing intraoperative imaging with respect to the task of registration.
机译:对于图像引导的程序,成像任务通常与将术中和术前图像配准到共同的坐标系有关。尽管此套准的准确性是系统性能的重要因素,但将套准准确性与图像质量因素(例如剂量,噪声和空间分辨率)相关的工作相对较少。为了创建这种关系的理论模型,我们提出了一种Fisher信息方法,以显式依赖于图像噪声,空间分辨率和信号功率谱等基础图像质量因子来分析配准性能。该模型对Cramer-Rao下界(CRLB)进行分析,其配准精度是控制图像质量的因素的函数。在计算机断层扫描低对比度软组织图像和高对比度骨骼(头部和颈部)图像的模拟中进行了实验,以比较测得的准确性[估计转换的均方根误差(RMSE)]与理论下限。对CRLB的分析表明,配准性能与互相关空间的信噪比密切相关。虽然下限是乐观的,但它显示出与实验结果一致的趋势,并产生了一种用于比较各种注册方法和相似性指标的性能的方法。进一步的分析验证了一种确定最佳配准后处理(图像过滤)的方法。提出了两个品质因数(CRLB和RMSE),它们将图像质量模型与配准性能相结合,为针对配准任务优化术中成像提供了重要指导。

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