Wachinger Christian, Wein Wolfgang, Navab Nassir
Computer Aided Medical Procedures (CAMP), TUM, München, Germany.
Acad Radiol. 2008 Nov;15(11):1404-15. doi: 10.1016/j.acra.2008.07.004.
The creation of two-dimensional (2D) ultrasound mosaics is becoming a common clinical practice with a high clinical value. The next step coming along with the increasing availability of 2D array transducers is the creation of three-dimensional mosaics. The correct alignment of multiple ultrasound images is, however, a complex task. In the literature of ultrasound registration, the alignment of two images has been often addressed, but not the alignment of multiple images. Therefore, we propose registration strategies for multiple image alignment and ultrasound specific similarity measures, which are able to cope with problems when aligning ultrasound images.
In this study, we investigate the following strategies for multiple image alignment: pairwise registration with a successive Lie group normalization and simultaneous registration, which urges the usage of multivariate similarity measures. We propose alternative multivariate extensions for similarity measures based on a maximum likelihood framework. Moreover, we take the inherent contamination of ultrasound images by speckle patterns into consideration by using alternative noise models based on multiplicative Rayleigh distributed noise. This leads us to ultrasound-specific similarity measures.
We compare the performances of pairwise and simultaneous registration approaches for the mosaicing scenario. Bivariate similarity measures are highly overlap-dependent, so that they rather favor the total overlap of the images than their correct alignment. Using ultrasound-specific bivariate measures leads to better results; however, a local optimum at the total overlap remains. In contrast, the derived multivariate similarity measures have a clear and single optimum at the correct alignment of the volumes.
The experiments indicate that standard, pairwise registration techniques have problems by aligning multiple ultrasound images with partial overlap. We demonstrate that the usage of an ultrasound specific similarity measure leads to better results for pairwise registration. The highest robustness, however, can be achieved by using simultaneous registration with the developed multivariate similarity measures.
二维（2D）超声拼图的创建正成为一种具有高临床价值的常见临床实践。随着二维阵列换能器的日益普及，下一步是创建三维拼图。然而，多个超声图像的正确对齐是一项复杂的任务。在超声配准文献中，经常讨论的是两幅图像的对齐，而不是多幅图像的对齐。因此，我们提出了用于多图像对齐的配准策略和超声特定的相似性度量，它们能够应对超声图像对齐时出现的问题。
在本研究中，我们研究了以下多图像对齐策略：使用连续李群归一化的成对配准和同时配准，后者促使使用多变量相似性度量。我们基于最大似然框架提出了相似性度量的替代多变量扩展。此外，我们通过使用基于乘性瑞利分布噪声的替代噪声模型，考虑了超声图像中散斑图案的固有污染。这使我们得到了超声特定的相似性度量。
我们比较了拼图场景中两两配准和同时配准方法的性能。双变量相似性度量高度依赖重叠，因此它们更倾向于图像的完全重叠而不是正确对齐。使用超声特定的双变量度量会得到更好的结果；然而，在完全重叠处仍存在局部最优。相比之下，导出的多变量相似性度量在体积的正确对齐处有一个清晰且唯一的最优值。
实验表明，标准的两两配准技术在对齐部分重叠的多个超声图像时存在问题。我们证明，使用超声特定的相似性度量在两两配准中能得到更好的结果。然而，通过使用与所开发的多变量相似性度量的同时配准可以实现最高的鲁棒性。
