Yanbin Hou

Sparse reconstruction for quantitative bioluminescence tomography based on the incomplete variables truncated conjugate gradient method

In this paper, we present an incomplete variables truncated conjugate gradient (IVTCG) method for bioluminescence tomography (BLT). Considering the sparse characteristic of the light source and insufficient surface measurement in the BLT scenarios, we combine a sparseness-inducing (ℓ1 norm) regularization term with a quadratic error term in the IVTCG-based framework for solving the inverse problem. By limiting the number of variables updated at each iterative and combining a variable splitting strategy to find the search direction more efficiently, it obtains fast and stable source reconstruction, even without a priori information of the permissible source region and multispectral measurements. Numerical experiments on a mouse atlas validate the effectiveness of the method. In vivo mouse experimental results further indicate its potential for a practical BLT system.

A source reconstruction algorithm based on adaptive hp-FEM for bioluminescence tomography

As a novel modality of molecular imaging, bioluminescence tomography (BLT) is used to in vivo observe and measure the biological process at cellular and molecular level in small animals. The core issue of BLT is to determine the distribution of internal bioluminescent sources from optical measurements on external surface. In this paper, a new algorithm is presented for BLT source reconstruction based on adaptive hp-finite element method. Using adaptive mesh refinement strategy and intelligent permissible source region, we can obtain more accurate information about the location and density of sources, with the robustness, stability and efficiency improved. Numerical simulations and physical experiment were both conducted to verify the performance of the proposed algorithm, where the optical data on phantom surface were obtained via Monte Carlo simulation and CCD camera detection, respectively. The results represent the merits and potential of our algorithm for BLT source reconstruction.

Truncated total least squares method with a practical truncation parameter choice scheme for bioluminescence tomography inverse problem

In bioluminescence tomography (BLT), reconstruction of internal bioluminescent source distribution from the surface optical signals is an ill-posed inverse problem. In real BLT experiment, apart from the measurement noise, the system errors caused by geometry mismatch, numerical discretization, and optical modeling approximations are also inevitable, which may lead to large errors in the reconstruction results. Most regularization techniques such as Tikhonov method only consider measurement noise, whereas the influences of system errors have not been investigated. In this paper, the truncated total least squares method (TTLS) is introduced into BLT reconstruction, in which both system errors and measurement noise are taken into account. Based on the modified generalized cross validation (MGCV) criterion and residual error minimization, a practical parameter-choice scheme referred to as improved GCV (IGCV) is proposed for TTLS. Numerical simulations with different noise levels and physical experiments demonstrate the effectiveness and potential of TTLS combined with IGCV for solving the BLT inverse problem.

Study of four regularization methods for the inverse problem in bioluminescence tomography

As a promising tool for in-vivo molecular imaging of small animals, Bioluminescence Tomography (BLT) aims at the quantitative reconstruction of the bioluminescent source distribution from the detected optical signals on the body surface. Mathematically, BLT is a highly ill-posed inverse problem per se. Most existing works are based on Tikhonov regularization in which the selection of the proper regular parameter is quite difficult. In this paper, two direct regularization methods, truncated singular value decomposition (TSVD) and truncated total least squares (TTLS), as well as two iterative regularization methods, conjugate gradient least squares (CGLS) and least squares QR decomposition (LSQR), are applied to the inverse problem in BLT, with the finite element method solving the diffusion equation. In the numerical simulation, a heterogeneous phantom is designed to compare and evaluate the four methods. The results show that all the four methods can reconstruct the position of bioluminescence sources accurately and are more convenient in the determination of regularization parameter than Tikhonov method. In addition, with a priori knowledge of the source permissible region employed in the reconstruction, the iterative methods are faster than the two direct methods. Among the four methods, LSQR performs quite stably when both model noise and measure noise are considered.

Mapping of bioluminescent images onto CT volume surface for dual-modality BLT and CT imaging

We present a method for mapping the two-dimensional (2D) bioluminescent images (BLIs) onto a three-dimensional (3D) body surface derived from the computed tomography (CT) volume data. This mapping includes two closely-related steps, the spatial registration of the 2D BLIs into the coordinate system of the CT volume data and the light flux recovering on the body surface from BLIs. By labeling markers on the body surface, we proposed an effective registration method to achieve the spatial position alignment. The subsequent light flux recovering is presented based on the inverse process of the free-space light transport model and taking the influence of the camera lens diaphragm into account. Incorporating the mapping procedure into the bioluminescence tomography (BLT) reconstruction, we developed a dual-modality BLT and CT imaging framework to provide both optical and anatomical information. The accuracy of the registration and the light flux recovering methods were evaluated via physical phantom experiments. The registration method was found to have a mean error of 0.41 mm and 0.35 mm in horizontal and vertical direction, and the accuracy of the light flux recovering method was below 5%. Furthermore, we evaluated the performance of the dual-modality BLT/CT imaging framework using a mouse phantom. Preliminary results revealed the potential and feasibility of the dual-modality imaging framework.

Bimodal BLT source reconstruction based on adjoint diffusion equations

As one of molecular imaging, bioluminescence tomography (BLT) aims to recover internal source from surface measurement. Being an ill-posed inverse problem, BLT source reconstruction is usually converted to an optimization problem through regularization. In this contribution, we build a bimodal hybrid imaging system consisting of BLT and micro-CT, and then propose an improved source reconstruction method based on adjoint diffusion equations (ADEs). Compared with conventional methods based on constrained minimization problem (CMP), ADEs-based method replaces expensive iterative computation with solving a group of linear ADEs. Given surface flux density, internal source power density and photon fluence rate can be efficiently determined in one step. Both numerical and physical experiments are performed to evaluate the bimodal BLT/micro-CT imaging system and this novel reconstruction method. The relevant results demonstrate the feasibility and potential of this source reconstruction method.

Lateral inhibition network model optimization by evolutionary strategy for image segmentation

Image segmentation is a fundamental image processing technology. There are many kinds of image segmentation methods, but most of them are problem oriented. In this paper, image segmentation method based on lateral inhibition network is presented. Lateral inhibition network is a biological vision model. When an image is filtered by a lateral inhibition network, its low frequency components are inhibited while the high frequency components are enhanced. The lateral inhibited image is much easier to be segmented because of its increased inter-class difference and decreased intra-class difference. The parameters of the lateral inhibition network model determine the inhibited image, thus affect the image segmentation result greatly. But there are no assured rules to determine the parameters. We propose an evolutionary strategy (ES) based method to search the optimal weighting parameters of the lateral inhibition network model. The objective function of ES is a multiattribute fitness function that combines multiple criteria of clustering and entropy information. The original image is filtered using the optimal lateral inhibition network and then the inhibited image is segmented by an optimized threshold. Using test images of various characteristics, the proposed method is evaluated by four objective image segmentation evaluation indexes. The experimental results show its validity and universality.