Regularization functional semi-automated incorporation of anatomical prior information in image-guided fluorescence tomography.
Regularization functional semi-automated incorporation of anatomical prior information in image-guided fluorescence tomography.
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DOI：
10.1364/ol.38.002407
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发表时间：
2013-07
期刊：
影响因子：
3.6
通讯作者：
R. Holt;S. Davis;B. Pogue
中科院分区：
文献类型：
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作者：
R. Holt;S. Davis;B. Pogue
众所周知，在荧光断层扫描中使用解剖学先验知识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来实现的，这一过程需要大量的时间和专业知识。我们提出了一种自动实现方法，将灰度先验图像直接编码为正则化项，从而消除了直接先验图像分割的需要，该分割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。
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.