Paper
7 March 2016 Direct reconstruction of pharmacokinetic parameters in dynamic fluorescence molecular tomography by the augmented Lagrangian method
Author Affiliations +
Proceedings Volume 9701, Multimodal Biomedical Imaging XI; 97010R (2016) https://doi.org/10.1117/12.2210745
Event: SPIE BiOS, 2016, San Francisco, California, United States
Abstract
Dynamic fluorescence molecular tomography (FMT) has the potential to quantify physiological or biochemical information, known as pharmacokinetic parameters, which are important for cancer detection, drug development and delivery etc. To image those parameters, there are indirect methods, which are easier to implement but tend to provide images with low signal-to-noise ratio, and direct methods, which model all the measurement noises together and are statistically more efficient. The direct reconstruction methods in dynamic FMT have attracted a lot of attention recently. However, the coupling of tomographic image reconstruction and nonlinearity of kinetic parameter estimation due to the compartment modeling has imposed a huge computational burden to the direct reconstruction of the kinetic parameters. In this paper, we propose to take advantage of both the direct and indirect reconstruction ideas through a variable splitting strategy under the augmented Lagrangian framework. Each iteration of the direct reconstruction is split into two steps: the dynamic FMT image reconstruction and the node-wise nonlinear least squares fitting of the pharmacokinetic parameter images. Through numerical simulation studies, we have found that the proposed algorithm can achieve good reconstruction results within a small amount of time. This will be the first step for a combined dynamic PET and FMT imaging in the future.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Dianwen Zhu, Wei Zhang, Yue Zhao, and Changqing Li "Direct reconstruction of pharmacokinetic parameters in dynamic fluorescence molecular tomography by the augmented Lagrangian method", Proc. SPIE 9701, Multimodal Biomedical Imaging XI, 97010R (7 March 2016); https://doi.org/10.1117/12.2210745
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KEYWORDS
Tissues

Tomography

Signal to noise ratio

Liver

Fluorescence tomography

Luminescence

Reconstruction algorithms

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