Probability method for Cerenkov luminescence tomography based on conformance error minimization

Abstract

Cerenkov luminescence tomography (CLT) was developed to reconstruct a three-dimensional (3D) distribution of radioactive probes inside a living animal. Reconstruction methods are generally performed within a unique framework by searching for the optimum solution. However, the ill-posed aspect of the inverse problem usually results in the reconstruction being non-robust. In addition, the reconstructed result may not match reality since the difference between the highest and lowest uptakes of the resulting radiotracers may be considerably large, therefore the biological significance is lost. In this paper, based on the minimization of a conformance error, a probability method is proposed that consists of qualitative and quantitative modules. The proposed method first pinpoints the organ that contains the light source. Next, we developed a 0-1 linear optimization subject to a space constraint to model the CLT inverse problem, which was transformed into a forward problem by employing a region growing method to solve the optimization. After running through all of the elements used to grow the sources, a source sequence was obtained. Finally, the probability of each discrete node being the light source inside the organ was reconstructed. One numerical study and two in vivo experiments were conducted to verify the performance of the proposed algorithm, and comparisons were carried out using the hp-finite element method (hp-FEM). The results suggested that our proposed probability method was more robust and reasonable than hp-FEM.

Mathematical method in optical molecular imaging

“…In order to further improve the accuracy and efficiency for reconstruction, it is essential to use the iterative solution method to obtain meaningful solutions. The optimization problems of the objective function in (34) are solved by using the following iterative solution methods to improve the accuracy and efficiency for reconstruction such as the Newton method [96], conjugate gradient method [85,97], augmented Lagrangian method [98], primal-dual interior-point method [99], iterative shrinkage method [100,101], Split Bregman method [88,[102][103][104], projection method [86,95,105] and probability method [76,106]. In order to test the performance of the regularization methods and iterative solution methods, Figure 3 has been given http://engine.scichina.com/doi/10.1007/s11432-014-5222-5 depicting the experimental results of the subspace pursuit method that has been employed for solving the sparse regularization.…”

Non-Negative Iterative Convex Refinement Approach for Accurate and Robust Reconstruction in Cerenkov Luminescence Tomography

“…The 3D anatomical imaging modality, such as computed tomography (CT) or magnetic resonance imaging (MRI), provides a spatial outline of the imaging subjects. Based on the outline and Cerenkov luminescence images, the 3D distribution of Cerenkov source is acquired by solving the diffusion equation [14]. It is considered that CLT is a highly promising imaging modality for clinical applications owing to its semi-quantitative analysis capability of in vivo radiopharmaceuticals distribution [15].…”

Adaptively Hybrid $3^{\text{rd}}$ Simplified Spherical Harmonics With Diffusion Equation-Based Multispectral Cerenkov Luminescence Tomography

“…CLT can reconstruct the distribution of internal radionuclides using the surface measurements [20]- [24]. The concept of CLT was first proposed by Li et al to reconstruct the 3D distribution 18 FDG in a homogeneous mouse model [20].…”