IEEE Trans Med Imaging. 2015 Jul;34(7):1602-1615. doi: 10.1109/TMI.2015.2407891. Epub 2015 Feb 27.
Diffuse optical tomography (DOT) is a non-invasive imaging technique to reconstruct optical properties of biological tissues using near-infrared light, and it has been successfully used to measure functional brain activities via changes in cerebral blood volume and cerebral blood oxygenation. However, DOT presents a severely ill-posed inverse problem, so various types of regularization should be incorporated to overcome low spatial resolution and lack of depth sensitivity. Another limitation of the conventional DOT reconstruction methods is that an inference step is separately performed after the reconstruction, so complicated interaction between reconstruction and regularization is difficult to analyze. To overcome these technical difficulties, we propose a unified sparse recovery framework using a random effect model whose termination criterion is determined by the statistical inference. Both numerical and experimental results confirm that the proposed method outperforms the conventional approaches.
扩散光学断层扫描（DOT）是一种非侵入性成像技术，它利用近红外光来重建生物组织的光学特性，并且已成功用于通过脑血容量和脑血氧合的变化来测量大脑功能活动。然而，DOT存在严重的不适定逆问题，因此应采用各种类型的正则化来克服空间分辨率低和缺乏深度敏感性的问题。传统DOT重建方法的另一个局限性是，在重建后单独执行推理步骤，因此难以分析重建与正则化之间复杂的相互作用。为了克服这些技术难题，我们提出了一种使用随机效应模型的统一稀疏恢复框架，其终止准则由统计推断确定。数值和实验结果均证实，所提出的方法优于传统方法。
