Total variation regularization for nonlinear fluorescence tomography with an augmented Lagrangian splitting approach

Applied Optics
Manuel FreibergerHermann Scharfetter

Abstract

Fluorescence tomography is an imaging modality that seeks to reconstruct the distribution of fluorescent dyes inside a highly scattering sample from light measurements on the boundary. Using common inversion methods with L(2) penalties typically leads to smooth reconstructions, which degrades the obtainable resolution. The use of total variation (TV) regularization for the inverse model is investigated. To solve the inverse problem efficiently, an augmented Lagrange method is utilized that allows separating the Gauss-Newton minimization from the TV minimization. Results on noisy simulation data provide evidence that the reconstructed inclusions are much better localized and that their half-width measure decreases by at least 25% compared to ordinary L(2) reconstructions.

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Citations

Nov 4, 2011·Medical Physics·J F P-J AbascalM Desco
Jan 3, 2013·The Review of Scientific Instruments·Shilpa Dilipkumar, Partha Pratim Mondal
Aug 29, 2012·Computational and Mathematical Methods in Medicine·Wei GuoJie Tian
Dec 29, 2016·IEEE Transactions on Medical Imaging·Dmytro ShulgaPatrick Hunziker
Jun 13, 2014·Optics Express·Guanghua GongMinyu Yao
Apr 22, 2017·Journal of Biomedical Optics·Xuelei HeXiaowei He
Jun 16, 2015·Optics Express·Kan WangXiaoquan Yang
Jul 3, 2015·Biomedical Optics Express·Hongbo GuoShuling Zhang
Jul 19, 2012·Biomedical Optics Express·Patricia BrunnerHermann Scharfetter
Mar 11, 2017·Computational and Mathematical Methods in Medicine·Jinping TangLi Li
Sep 5, 2018·Visual Computing for Industry, Biomedicine and Art·Yu AnJie Tian
Jan 9, 2021·Molecular Imaging and Biology : MIB : the Official Publication of the Academy of Molecular Imaging·Xuelei HeJie Tian

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