Search

Image reconstruction with limited angles projection data is a challenging task in computed tomography (CT). The amount of radiation associated with CT induces health implications to the patient. Besides, image reconstruction with limited-angles projection data distorts the image, thus emasculating the efficiency of diagnosis. Also, the poly-chromatic nature of the X-ray adds beam-hardening artifacts in the reconstruction. The state-of-the-art approaches available in the literature have proposed the solutions for beam-hardening artifacts correction in full span computed tomography. Most of the solutions are hardware based and need extra hardware to remove the beam hardening artifacts. The present manuscript proposes artificial intelligence based software solution for the beam hardening artifacts removal. This manuscript has presented a cascaded encoder-decoder architecture named cascaded deep neural network for image reconstruction (CDNN). The CDNN architecture has convolution neural network blocks that include convolution layers, rectified linear units ReLU, and batch normalization layers. The network has skip-connections for better learning of features between input and output. The network has been designed as a forward model. The stochastic gradient descent optimization method has been used for training the network. Image reconstructed from Fourier transform-based approach has been used as a prior. A novel approach for reduction of beam-hardening artifacts in case of limited-angles computed tomography using CDNN has been presented. The proposed approach is comparable to other hardware/software solutions for aforesaid purpose and does not require any extra hardware. The proposed approach has improved the image quality as compared to U-Net and the other state-of-the-art methods. It has been found from the experiments that the CDNN suppresses the artifacts and improves the reconstruction. The performance of the proposed CDNN has been tested with real-life data having beam hardening artifacts. It has been observed that the CDNN has improved the reconstruction quality by reducing streak, ring artifacts, and beam hardening artifacts and also preserving the profound structures. [ABSTRACT FROM AUTHOR]

If a spatial-domain function has a finite support, its Fourier transform is an entire function. The Taylor series expansion of an entire function converges at every finite point in the complex plane. The analytic continuation theory suggests that a finite-sized object can be uniquely determined by its frequency components in a very small neighborhood. Trying to obtain such an exact Taylor expansion is difficult. This paper proposes an iterative algorithm to extend the measured frequency components to unmeasured regions. Computer simulations show that the proposed algorithm converges very slowly, indicating that the problem is too ill-posed to be practically solvable using available methods. [ABSTRACT FROM AUTHOR]

Purpose: The application of traditional machine learning techniques, in the form of regression models based on conventional, "hand-crafted" features, to artifact reduction in limited angle tomography is investigated.Methods: Mean-variation-median (MVM), Laplacian, Hessian, and shift-variant data loss (SVDL) features are extracted from the images reconstructed from limited angle data. The regression models linear regression (LR), multilayer perceptron (MLP), and reduced-error pruning tree (REPTree) are applied to predict artifact images.Results: REPTree learns artifacts best and reaches the smallest root-mean-square error (RMSE) of 29 HU for the Shepp-Logan phantom in a parallel-beam study. Further experiments demonstrate that the MVM and Hessian features complement each other, whereas the Laplacian feature is redundant in the presence of MVM. In fan-beam, the SVDL features are also beneficial. A preliminary experiment on clinical data in a fan-beam study demonstrates that REPTree can reduce some artifacts for clinical data. However, it is not sufficient as a lot of incorrect pixel intensities still remain in the estimated reconstruction images.Conclusion: REPTree has the best performance on learning artifacts in limited angle tomography compared with LR and MLP. The features of MVM, Hessian, and SVDL are beneficial for artifact prediction in limited angle tomography. Preliminary experiments on clinical data suggest that the investigation on more features is necessary for clinical applications of REPTree. [ABSTRACT FROM AUTHOR]

We introduce a cone-beam computed tomography with insufficient projections obtained from a limited-angle scan, the socalled digital tomosynthesis, and demonstrate its feasibility for the industrial applications by implementing to the reconstruction of internal slice images of a multilayer printed circuit board. The image reconstruction algorithm is based on the filtered-backprojection approach for the cone-beam geometry with isocentric linear motion in the scanning trajectory of the X-ray source and imaging detector pair. Although the slice image reconstructed at the plane of interest was affected by the structures outside, we could reconstruct the plane of interest with the total scan angle less than 20 degrees and 11 projections. The digital tomosynthesis is expected to be practical for industrial tomography restricted to the limited-angle scan. [ABSTRACT FROM AUTHOR]

A new iterative algorithm of tomographic reconstruction of objects on the basis of projection data available in a limited range of angles only is proposed. The algorithm is based on calculating artificial projections in those directions where projection data are unavailable. By means of numerical simulations, it is verified that the algorithm developed ensured high quality of reconstruction up to the angular interval of 45–60°. [ABSTRACT FROM AUTHOR]