Open AccessImproving mesoscopic fluorescence molecular tomography via preconditioning and regularization
Author(s) -
Fuzheng Yang,
Ru Yao,
Mehmet S. Ozturk,
Denzel Faulkner,
Qinglan Qu,
Xavier Intes
Publication year - 2018
Publication title -
biomedical optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.9.002765
Subject(s) - inverse problem , sensitivity (control systems) , compressed sensing , iterative reconstruction , rate of convergence , preconditioner , mesoscopic physics , algorithm , regularization (linguistics) , computer science , jacobian matrix and determinant , solver , matrix (chemical analysis) , mathematical optimization , iterative method , mathematics , materials science , physics , artificial intelligence , channel (broadcasting) , mathematical analysis , computer network , quantum mechanics , electronic engineering , engineering , composite material
Mesoscopic fluorescence molecular tomography (MFMT) is a novel imaging technique capable of obtaining 3-D distribution of molecular probes inside biological tissues at depths of a few millimeters with a resolution up to ~100 μm. However, the ill-conditioned nature of the MFMT inverse problem severely deteriorates its reconstruction performances. Furthermore, dense spatial sampling and fine discretization of the imaging volume required for high resolution reconstructions make the sensitivity matrix (Jacobian) highly correlated, which prevents even advanced algorithms from achieving optimal solutions. In this work, we propose two computational methods to respectively increase the incoherence of the sensitivity matrix and improve the convergence rate of the inverse solver. We first apply a compressed sensing (CS) based preconditioner on either the whole sensitivity matrix or sub sensitivity matrices to reduce the coherence between columns of the sensitivity matrix. Then we employed a regularization method based on the weight iterative improvement method (WIIM) to mitigate the ill-condition of the sensitivity matrix and to drive the iterative optimization process towards convergence at a faster rate. We performed numerical simulations and phantom experiments to validate the effectiveness of the proposed strategies. In both in silico and in vitro cases, we were able to improve the quality of MFMT reconstructions significantly.