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Fundamental limits of image registration performance: Effects of image noise and resolution in CT-guided interventions

机译:图像配准性能的基本限制:CT引导干预中图像噪声和分辨率的影响

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Purpose: In image-guided procedures, image acquisition is often performed primarily for the task of geometrically registering information from another image dataset, rather than detection / visualization of a particular feature. While the ability to detect a particular feature in an image has been studied extensively with respect to image quality characteristics (noise, resolution) and is an ongoing, active area of research, comparatively little has been accomplished to relate such image quality characteristics to registration performance. Methods: To establish such a framework, we derived Cramer-Rao lower bounds (CRLB) for registration accuracy, revealing the underlying dependencies on image variance and gradient strength. The CRLB was analyzed as a function of image quality factors (in particular, dose) for various similarity metrics and compared to registration accuracy using CT images of an anthropomorphic head phantom at various simulated dose levels. Performance was evaluated in terms of root mean square error (RMSE) of the registration parameters. Results: Analysis of the CRLB shows two primary dependencies: 1) noise variance (related to dose); and 2) sum of squared image gradients (related to spatial resolution and image content). Comparison of the measured RMSE to the CRLB showed that the best registration method, RMSE achieved the CRLB to within an efficiency factor of 0.21, and optimal estimators followed the predicted inverse proportionality between registration performance and radiation dose. Conclusions: Analysis of the CRLB for image registration is an important step toward understanding and evaluating an intraoperative imaging system with respect to a registration task. While the CRLB is optimistic in absolute performance, it reveals a basis for relating the performance of registration estimators as a function of noise content and may be used to guide acquisition parameter selection (e.g., dose) for purposes of intraoperative registration.
机译:目的:在图像引导的过程中,通常主要是为了以几何方式注册来自另一个图像数据集的信息,而不是对特定特征进行检测/可视化,来执行图像获取。虽然已经针对图像质量特性(噪声,分辨率)对图像中的特定特征进行检测的能力进行了广泛研究,并且这是一个持续不断的研究领域,但将这种图像质量特性与配准性能相关联的工作却很少。 。方法:为了建立这样的框架,我们推导了Cramer-Rao下界(CRLB)以提高配准精度,揭示了对图像方差和梯度强度的潜在依赖性。对于各种相似性指标,将CRLB作为图像质量因子(特别是剂量)的函数进行分析,并使用拟人化头部模型的CT图像在各种模拟剂量水平下与配准精度进行比较。根据注册参数的均方根误差(RMSE)评估了性能。结果:CRLB的分析显示出两个主要依赖性:1)噪声方差(与剂量有关);和2)平方的图像梯度之和(与空间分辨率和图像内容有关)。将测得的RMSE与CRLB进行比较表明,最佳配准方法RMSE达到CRLB的效率系数在0.21之内,并且最佳估计量遵循配准性能与辐射剂量之间的预测反比例关系。结论:对于图像配准的CRLB分析是朝着针对配准任务理解和评估术中成像系统的重要一步。尽管CRLB在绝对性能方面是乐观的,但它揭示了根据噪声含量将配准估计量的性能关联起来的基础,并且可以用于指导术中配准的采集参数选择(例如剂量)。

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