Zhang Hanming

Image reconstruction based on total-variation minimization and alternating direction method in linear scan computed tomography

Linear scan computed tomography (LCT) is of great benefit to online industrial scanning and security inspection due to its characteristics of straight-line source trajectory and high scanning speed. However, in practical applications of LCT, there are challenges to image reconstruction due to limited-angle and insufficient data. In this paper, a new reconstruction algorithm based on total-variation (TV) minimization is developed to reconstruct images from limited-angle and insufficient data in LCT. The main idea of our approach is to reformulate a TV problem as a linear equality constrained problem where the objective function is separable, and then minimize its augmented Lagrangian function by using alternating direction method (ADM) to solve subproblems. The proposed method is robust and efficient in the task of reconstruction by showing the convergence of ADM. The numerical simulations and real data reconstructions show that the proposed reconstruction method brings reasonable performance and outperforms some previous ones when applied to an LCT imaging problem.

Fast parallel algorithm for three-dimensional distance-driven model in iterative computed tomography reconstruction

The projection matrix model is used to describe the physical relationship between reconstructed object and projection. Such a model has a strong influence on projection and backprojection, two vital operations in iterative computed tomographic reconstruction. The distance-driven model (DDM) is a state-of-the-art technology that simulates forward and back projections. This model has a low computational complexity and a relatively high spatial resolution; however, it includes only a few methods in a parallel operation with a matched model scheme. This study introduces a fast and parallelizable algorithm to improve the traditional DDM for computing the parallel projection and backprojection operations. Our proposed model has been implemented on a GPU (graphic processing unit) platform and has achieved satisfactory computational efficiency with no approximation. The runtime for the projection and backprojection operations with our model is approximately 4.5 s and 10.5 s per loop, respectively, with an image size of 256×256×256 and 360 projections with a size of 512×512. We compare several general algorithms that have been proposed for maximizing GPU efficiency by using the unmatched projection/backprojection models in a parallel computation. The imaging resolution is not sacrificed and remains accurate during computed tomographic reconstruction.

Matrix approach for processing of iterative reconstruction on cone beam CT

Cone-beam computed tomography (CBCT) is an important technique providing new insights into the inner structure of products in industry and medicine physics. Iterative reconstruction methods have been shown to be more robust than analytical algorithm against the noise and limited angles conditions present in CT. Nevertheless, these methods are not extensively used due to their computational demands. In the iteration algorithm, the matrix of projection is massive and it is very time-consuming to calculate the forward projections and back-projections. In this work, we design a matrix approach that the coefficients of the projection matrix are pre-calculated and simultaneously stored with two compressing formats due to the different sparse structures of the matrix and its transposed matrix. And we implement the corresponding SpMV (sparse matrix-vector multiplication) based on the compressing matrices with GPU platform to realize the acceleration. Experimental results indicate that this method allows efficient implementations of reconstruction in CBCT and it can have a better performance than those with serial computing on CPU.