Depth compensation in fluorescence molecular tomography using an adaptive support driven reweighted L1-minimization algorithm

Junwei Shi; Fei Liu; Jianwen Luo; Jing Bai

doi:10.1117/12.2060171

19 September 2014 Depth compensation in fluorescence molecular tomography using an adaptive support driven reweighted L1-minimization algorithm

Junwei Shi, Fei Liu, Jianwen Luo, Jing Bai

Author Affiliations +

Proceedings Volume 9216, Optics and Photonics for Information Processing VIII; 921603 (2014) https://doi.org/10.1117/12.2060171
Event: SPIE Optical Engineering + Applications, 2014, San Diego, California, United States

Abstract

In fluorescence molecular tomography (FMT), the fluorophore distribution is reconstructed using the diffuse-light measurements obtained from the rotating source-detector pairs placed on the boundary of the tissues. Owing to the intensity attenuation of light when it propagates through tissues, the sensitivity of measurements deteriorates quickly with increased depth. Thus the inconsistent contrast of reconstructed fluorophores located at different depths is a major challenge in FMT. As a spatially variant regularization method, the adaptive support driven reweighted L1-minimization (ASDR-L1) algorithm is proposed here for depth compensation in FMT. ASDR-L1 is a modification of the restarted L1 regularization-based nonlinear conjugate gradient (re-L1-NCG) algorithm previously proposed by our laboratory. In ASDR-L1, the original L1-minimization problem is replaced by a sequence of weighted L1-minimization subproblems with spatially updated weights applied to the adaptive support estimate. Like re-L1-NCG, ASRDR-L1 adopts the restarted strategy in each outer iteration, which contributes to the adaptive support estimate. The updated weights for the next iteration spatially depend on the current solution. In the support estimate, spatially updated weights mean different regularization parameters for different locations. A large regularization parameter in the weighted L1-minimization subproblem makes the results concentrate on a small number of large values, whereas a small regularization parameter tends to make the values be evenly distributed. Thus depth compensation in FMT is achieved through the iteratively updated weights. Simulation experiments are conducted to confirm the feasibility of ASDR-L1. Through ASDR-L1, the reconstructed contrast between two identical fluorophores located at different depths is increased from 1:0.43 to 1:0.96.

Citation Download Citation

Junwei Shi, Fei Liu, Jianwen Luo, and Jing Bai "Depth compensation in fluorescence molecular tomography using an adaptive support driven reweighted L1-minimization algorithm", Proc. SPIE 9216, Optics and Photonics for Information Processing VIII, 921603 (19 September 2014); https://doi.org/10.1117/12.2060171

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available