title: Sparsity-driven reconstruction for FDOT with anatomical priors.
abstract: 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.
authors.affiliation: Swiss Federal Institute of Technology of Lausanne, 1015 Lausanne, Switzerland. jean-charles.baritaux@epfl.ch
authors.forename: Jean-Charles
authors.initials: JC
authors.lastname: Baritaux
authors.forename: Kai
authors.initials: K
authors.lastname: Hassler
authors.forename: Martina
authors.initials: M
authors.lastname: Bucher
authors.forename: Sebanti
authors.initials: S
authors.lastname: Sanyal
authors.forename: Michael
authors.initials: M
authors.lastname: Unser
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D005456:Fluorescent Dyes; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D019047:Phantoms, Imaging; D012815:Signal Processing, Computer-Assisted; D041622:Tomography, Optical / Q000295:instrumentation / Q000379:methods*
chemical_list: D005456:Fluorescent Dyes