Department of Physics and Astronomy, Dartmouth College, New Hampshire 03755, USA.
Opt Lett. 2013 Jul 15;38(14):2407-9. doi: 10.1364/OL.38.002407.
The use of anatomical priors in fluorescence tomography is known to improve image quality and accuracy significantly. However, the use of prior information is often implemented by incorporating user segmented structural images into the optical reconstruction algorithm, a process requiring significant time and expertise. We propose an automated implementation which encodes the gray-scale prior image directly into the regularization term, eliminating the need for direct prior image segmentation, which is extendable to any spatially defined prior data. The proposed method is supported by in vivo studies.
在荧光层析成像中使用解剖先验信息已知可以显著提高图像质量和准确性。然而，先验信息的使用通常是通过将用户分割的结构图像合并到光学重建算法中实现的，这是一个需要大量时间和专业知识的过程。我们提出了一种自动化实现方法，该方法将灰度先验图像直接编码到正则化项中，从而消除了对直接先验图像分割的需求，并且可以扩展到任何空间定义的先验数据。所提出的方法得到了体内研究的支持。
