Sparsity-Driven Reconstruction for FDOT With Anatomical Priors
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 -norm penalization and total variation penalization. Results on synthetic and experimental data are presented.
- URL: http://bigwww.epfl.ch/publications/baritaux1102.html
- URL: http://bigwww.epfl.ch/publications/baritaux1102.pdf
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Keywords: Fluorescence imaging ; optical tomography ; optimization ; reconstruction ; Diffuse Optical Tomography ; In-Vivo ; Fluorescence Tomography ; Molecular Tomography ; Regularization ; Information ; Algorithm ; Breast
Record created on 2011-12-16, modified on 2016-08-09