X-ray luggage CT is widely used in airports and railway stations for the purpose of detecting contrabands and dangerous
goods that may be potential threaten to public safety, playing an important role in homeland security. An X-ray luggage
CT is usually in a helical trajectory with a high pitch for achieving a high passing speed of the luggage. The disadvantage
of high pitch is that conventional filtered back-projection (FBP) requires a very large slice thickness, leading to bad axial
resolution and helical artifacts. Especially when severe data inconsistencies are present in the z-direction, like the ends of
a scanning object, the partial volume effect leads to inaccuracy value and may cause a wrong identification. In this paper,
an iterative reconstruction method is developed to improve the image quality and accuracy for a large-spacing multi-detector
high-pitch helical luggage CT system. In this method, the slice thickness is set to be much smaller than the
pitch. Each slice involves projection data collected in a rather small angular range, being an ill-conditioned limited-angle
problem. Firstly a low-resolution reconstruction is employed to obtain images, which are used as prior images in the
following process. Then iterative reconstruction is performed to obtain high-resolution images. This method enables a
high volume coverage speed and a thin reconstruction slice for the helical luggage CT. We validate this method with data
collected in a commercial X-ray luggage CT.
In order to detect deformations of parts during the operating test, a novel dynamic industry computed tomography (ICT) system taking advantage of the rotation of specimens itself was purposed by us. However the stationary parts such as the shell around the turbine tips, which are hardly removed due to some industrial reasons, contaminate the projection data, so the blocks are not easily corrected from the projections as what we did in the traditional detector correction procedure. In this work, an interaction based CT reconstruction algorithm is purposed to deal the problem. First of all, we directly reconstruct the image with the contaminated projection data and an interactive match between the reconstructed image and the prior image is performed according to some obvious features. Then a forward-projection of the matched prior image with the practical geometric parameters is made. The block components in the projection data are estimated by calculating the average difference between the forward projections and the real projections of certain detectors. Finally, a new image can be reconstructed using the corrected data. Furthermore, the efficiency of the purposed algorithm is proved by both numerical simulation and practical experiments.
This work gives a new Compressed Sensing (CS) based Computed Tomography (CT) reconstruction method for limited angle problem. Currently CS based reconstruction methods are achieved by a minimizing process on the total variation (TV) of CT image under data consistency constraint. For limited-angle problem due to the missing range of projection views the strength of data consistency constraint becomes direction relevant. In our work a new anisotropic total variation (ATV) minimization method is proposed. Instead of using image TV as the minimization objective, an ATV objective is designed which is combined of multiple 1D directional TV with different weights according to the actual scanned angular range. Experiments with simulated data demonstrate the advantages of our approach relative to the standard CS based reconstruction methods.
The Poisson-like noise model has been widely used for noise suppression and image reconstruction in low dose computed tomography. Various noise estimation and suppression approaches have been developed and studied to enhance the image quality. Among them, the recently proposed generalized Anscombe transform (GAT) has been utilized to stabilize the variance of Poisson-Gaussian noise. In this paper, we present a variance estimation approach using GAT. After the transform, the projection data is denoised conventionally with an assumption that the noise variance is uniformly equals to 1. The difference of the original and the denoised projection is treated as pure noise and the global variance σ2 can be estimated from the residual difference. Thus, the final denoising step with the estimated σ2 is performed. The proposed approach is verified on a cone-beam CT system and demonstrated to obtain a more accurate estimation of the actual parameter. We also examine FBP algorithm with the two-step noise suppression in the projection domain using the estimated noise variance. Reconstruction results with simulated and practical projection data suggest that the presented approach could be effective in practical imaging applications.
Nowadays a famous way to solve Computed Tomography (CT) inverse problems is to consider a constrained minimization problem following the Compressed Sensing (CS) theory. The CS theory proves the possibility of sparse signal recovery using under sampled measurements which gives a powerful tool for CT problems that have incomplete measurements or contain heavy noise. Among current CS reconstruction methods, one widely accepted reconstruction framework is to perform a total variation (TV) minimization process and a data fidelity constraint process in an alternative way by two separate iteration loops. However because the two processes are done independently certain misbalance may occur which leads to either over-smoothed or noisy reconstructions. Moreover, such misbalance is usually difficult to adjust as it varies according to the scanning objects and protocols. In our work we try to make good balance between the minimization and the constraint processes by estimating the variance of image noise. First, considering that the noise of projection data follows a Poisson distribution, the Anscombe transform (AT) and its inversion is utilized to calculate the unbiased variance of the projections. Second, an estimation of image noise is given through a noise transform model from projections to the image. Finally a modified CS reconstruction method is proposed which guarantees the desired variance on the reconstructed image thus prevents the block-wising or over-noised caused by misbalanced constrained minimizations. Results show the advantage in both image quality and convergence speed.