Swiss Federal Institute of Technology of Lausanne, 1015 Lausanne, Switzerland.
IEEE Trans Med Imaging. 2011 May;30(5):1143-53. doi: 10.1109/TMI.2011.2136438. Epub 2011 Apr 15.
In this paper we propose a method based on (2, 1)-mixed-norm penalization for incorporating a structural prior in FDOT image reconstruction. The effect of (2, 1)-mixed-norm penalization is twofold: first, a sparsifying effect which isolates few anatomical regions where the fluorescent probe has accumulated, and second, a regularization effect inside the selected anatomical regions. After formulating the reconstruction in a variational framework, we analyze the resulting optimization problem and derive a practical numerical method tailored to (2, 1)-mixed-norm regularization. The proposed method includes as particular cases other sparsity promoting regularization methods such as l(1)-norm penalization and total variation penalization. Results on synthetic and experimental data are presented.
在本文中，我们提出了一种基于（2,1）混合范数惩罚的方法，将结构先验纳入 FDOT 图像重建中。（2,1）混合范数惩罚的效果有两方面：一是稀疏效果，它隔离了荧光探针积累的少数几个解剖区域；二是所选解剖区域内的正则化效果。在变分框架中对重建进行公式化后，我们分析了所得的优化问题，并推导出了一种针对（2,1）混合范数正则化的实用数值方法。所提出的方法包括其他促进稀疏性的正则化方法（如 l(1)-范数惩罚和全变差惩罚）作为特例。给出了合成数据和实验数据的结果。
