IDeA Laboratory, Department of Neurology, University of California-Davis, Davis, CA 95618, USA.
IEEE Trans Med Imaging. 2013 Feb;32(2):223-36. doi: 10.1109/TMI.2012.2220153. Epub 2012 Sep 21.
Tensor-based morphometry is a powerful tool for automatically computing longitudinal change in brain structure. Because of bias in images and in the algorithm itself, however, a penalty term and inverse consistency are needed to control the over-reporting of nonbiological change. These may force a tradeoff between the intrinsic sensitivity and specificity, potentially leading to an under-reporting of authentic biological change with time. We propose a new method incorporating prior information about tissue boundaries (where biological change is likely to exist) that aims to keep the robustness and specificity contributed by the penalty term and inverse consistency while maintaining localization and sensitivity. Results indicate that this method has improved sensitivity without increased noise. Thus it will have enhanced power to detect differences within normal aging and along the spectrum of cognitive impairment.
基于张量的形态计量学是一种强大的工具，可用于自动计算大脑结构的纵向变化。然而，由于图像和算法本身存在偏差，需要惩罚项和逆一致性来控制对非生物变化的过度报告。这些可能会在固有敏感性和特异性之间产生权衡，随着时间的推移，可能会导致真实生物变化的报告不足。我们提出了一种新的方法，该方法结合了关于组织边界的先验信息（生物变化可能存在的地方），旨在保持惩罚项和逆一致性所带来的稳健性和特异性，同时保持定位和敏感性。结果表明，该方法在提高敏感性的同时没有增加噪声。因此，它将具有更高的能力来检测正常衰老过程中的差异以及认知障碍的频谱。
