IEEE Trans Med Imaging. 2013 Oct;32(10):1939-51. doi: 10.1109/TMI.2013.2269814. Epub 2013 Jul 3.
Longitudinal imaging studies are frequently used to investigate temporal changes in brain morphology and often require spatial correspondence between images achieved through image registration. Beside morphological changes, image intensity may also change over time, for example when studying brain maturation. However, such intensity changes are not accounted for in image similarity measures for standard image registration methods. Hence, 1) local similarity measures, 2) methods estimating intensity transformations between images, and 3) metamorphosis approaches have been developed to either achieve robustness with respect to intensity changes or to simultaneously capture spatial and intensity changes. For these methods, longitudinal intensity changes are not explicitly modeled and images are treated as independent static samples. Here, we propose a model-based image similarity measure for longitudinal image registration that estimates a temporal model of intensity change using all available images simultaneously.
纵向成像研究常用于研究脑形态随时间的变化，通常需要通过图像配准来实现图像之间的空间对应。除了形态变化外，图像强度也可能随时间发生变化，例如在研究大脑成熟度时。然而，标准图像配准方法的图像相似性度量并未考虑到这种强度变化。因此，人们开发了 1）局部相似性度量，2）估计图像之间强度变换的方法，以及 3）变形方法，以实现对强度变化的稳健性，或同时捕捉空间和强度变化。对于这些方法，纵向强度变化并未被明确建模，并且图像被视为独立的静态样本。在这里，我们提出了一种基于模型的纵向图像配准图像相似性度量方法，该方法使用所有可用图像同时估计强度变化的时间模型。
