Abstract
Fluorescence molecular tomography (FMT) is a promising imaging modality and has been actively studied in the past two decades since it can locate the specific tumor position three-dimensionally in small animals. However, it remains a challenging task to obtain fast, robust and accurate reconstruction of fluorescent probe distribution in small animals due to the large computational burden, the noisy measurement and the ill-posed nature of the inverse problem. In this paper we propose a nonuniform preconditioning method in combination with L (1) regularization and ordered subsets technique (NUMOS) to take care of the different updating needs at different pixels, to enhance sparsity and suppress noise, and to further boost convergence of approximate solutions for fluorescence molecular tomography. Using both simulated data and phantom experiment, we found that the proposed nonuniform updating method outperforms its popular uniform counterpart by obtaining a more localized, less noisy, more accurate image. The computational cost was greatly reduced as well. The ordered subset (OS) technique provided additional 5 times and 3 times speed enhancements for simulation and phantom experiments, respectively, without degrading image qualities. When compared with the popular L (1) algorithms such as iterative soft-thresholding algorithm (ISTA) and Fast iterative soft-thresholding algorithm (FISTA) algorithms, NUMOS also outperforms them by obtaining a better image in much shorter period of time.
Highlights
In the past two decades, fluorescence molecular tomography (FMT) has been playing an important role in a number of preclinical research fields [1, 2]
In medical imaging arguably the most well known application of MM framework was originated in Fessler et al [10] and Erdogan & Fessler [11] for transmission tomography, where separable quadratic surrogate (SQS) functions were introduced based on a uniform additive type of weight functions
We follow the spirit of [12, 13] and propose a non-uniform multiplicative weighting with ordered subsets (NUMOS) technique, which is a generalization of the image space reconstruction algorithm (ISRA) [14, 15], for the MM algorithm in FMT in small animal imaging, and validate its advantages over its uniform counterpart using both simulated data and phantom experiment
Summary
In the past two decades, fluorescence molecular tomography (FMT) has been playing an important role in a number of preclinical research fields [1, 2]. In the past two decades, the majorization-minimization (MM) algorithm has attracted considerable attention in medical imaging due to its advantages such as separating the high dimensional variable for an easier iterative update in a parallel way [9] and applying the nonnegative constraints straightforwardly. In medical imaging arguably the most well known application of MM framework was originated in Fessler et al [10] and Erdogan & Fessler [11] for transmission tomography, where separable quadratic surrogate (SQS) functions were introduced based on a uniform additive type of weight functions. In FMT, Dutta et al [5] followed [10, 11] and employed the uniform additive weight function to study the effects of the joint L1 and total variation regularization method. We follow the spirit of [12, 13] and propose a non-uniform multiplicative weighting with ordered subsets (NUMOS) technique, which is a generalization of the image space reconstruction algorithm (ISRA) [14, 15], for the MM algorithm in FMT in small animal imaging, and validate its advantages over its uniform counterpart using both simulated data and phantom experiment
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