Indexed by:
期刊论文
First Author:
Liu, Xin
Correspondence Author:
Liu, X (reprint author), Shanghai Univ, Sch Commun & Informat Engn, Shanghai 200444, Peoples R China.
Co-author:
Wang, Hongkai,Yan, Zhuangzhi
Date of Publication:
2016-11-01
Journal:
BIOMEDICAL OPTICS EXPRESS
Included Journals:
PubMed、SCIE、EI、Scopus
Document Type:
J
Volume:
7
Issue:
11
Page Number:
4527-4542
ISSN No.:
2156-7085
Key Words:
(170.3010) Image reconstruction techniques,(170.3880) Medical and biological imaging,(170.6280) Spectroscopy, fluorescence and luminescence,(170.6960) Tomography
Abstract:
Dynamic fluorescence molecular tomography (FMT) plays an important role in drug delivery research. However, the majority of current reconstruction methods focus on solving the stationary FMT problems. If the stationary reconstruction methods are applied to the time-varying fluorescence measurements, the reconstructed results may suffer from a high level of artifacts. In addition, based on the stationary methods, only one tomographic image can be obtained after scanning one circle projection data. As a result, the movement of fluorophore in imaged object may not be detected due to the relative long data acquisition time (typically > 1 min). In this paper, we apply extended kalman filter (EKF) technique to solve the non-stationary fluorescence tomography problem. Especially, to improve the EKF reconstruction performance, the generalized inverse of kalman gain is calculated by a second-order iterative method. The numerical simulation, phantom, and in vivo experiments are performed to evaluate the performance of the method. The experimental results indicate that by using the proposed EKF-based second-order iterative (EKF-SOI) method, we cannot only clearly resolve the time-varying distributions of fluorophore within imaged object, but also greatly improve the reconstruction time resolution (similar to 2.5 sec/frame) which makes it possible to detect the movement of fluorophore during the imaging processes. (C) 2016 Optical Society of America
Translation or Not:
no
