Review of the influence of noise in X-ray computed tomography measurement uncertainty

Different aspects of noise in X-ray computed tomography (XCT) for industrial purposes are examined. An overview of the most common noise metrics is given, together with a description of XCT noise influence quantities. We address the current state of the art in understanding the contribution of noise to XCT measurement uncertainty, giving a chronological view of the different attempts that have been made to account for the contribution from noise to XCT measurement uncertainty. We conclude that approaches to estimating the contribution of noise to XCT measurement uncertainty that account for not only noise, but also other factors that affect image quality (e.g., scattering, beam hardening and blurring) are preferable to approaches that only account for noise

Geometrical Calibration and Filter Optimization for Cone-Beam Computed Tomography

This thesis will discuss the requirements of a software library for tomography and will derive a framework which can be used to realize various applications in cone-beam computed tomography (CBCT). The presented framework is self-contained and is realized using the MATLAB environment in combination with native low-level technologies (C/C++ and CUDA) to improve its computational performance, while providing accessibility and extendability through to use of a scripting language environment. On top of this framework, the realization of Katsevich’s algorithm on multicore hardware will be explained and the resulting implementation will be compared to the Feldkamp, Davis and Kress (FDK) algorithm. It will also be shown that this helical reconstruction method has the potential to reduce the measurement uncertainty. However, misalignment artifacts appear more severe in the helical reconstructions from real data than in the circular ones. Especially for helical CBCT (H-CBCT), this fact suggests that a precise calibration of the computed tomography (CT) system is inevitable. As a consequence, a self-calibration method will be designed that is able to estimate the misalignment parameters from the cone-beam projection data without the need of any additional measurements. The presented method employs a multi-resolution 2D-3D registration technique and a novel volume update scheme in combination with a stochastic reprojection strategy to achieve a reasonable runtime performance. The presented results will show that this method reaches sub-voxel accuracy and can compete with current state-of-the-art online- and offline-calibration approaches. Additionally, for the construction of filters in the area of limited-angle tomography a general scheme which uses the Approximate Inverse (AI) to compute an optimized set of 2D angle-dependent projection filters will be derived. Optimal sets of filters are then precomputed for two angular range setups and will be reused to perform various evaluations on multiple datasets with a filtered backprojection (FBP)-type method. This approach will be compared to the standard FDK algorithm and to the simultaneous iterative reconstruction technique (SIRT). The results of the study show that the introduced filter optimization produces results comparable to those of SIRT with respect to the reduction of reconstruction artifacts, whereby its runtime is comparable to that of the FDK algorithm

Time dependent cone-beam CT reconstruction via a motion model optimized with forward iterative projection matching

The purpose of this work is to present the development and validation of a novel method for reconstructing time-dependent, or 4D, cone-beam CT (4DCBCT) images. 4DCBCT can have a variety of applications in the radiotherapy of moving targets, such as lung tumors, including treatment planning, dose verification, and real time treatment adaptation. However, in its current incarnation it suffers from poor reconstruction quality and limited temporal resolution that may restrict its efficacy. Our algorithm remedies these issues by deforming a previously acquired high quality reference fan-beam CT (FBCT) to match the projection data in the 4DCBCT data-set, essentially creating a 3D animation of the moving patient anatomy. This approach combines the high image quality of the FBCT with the fine temporal resolution of the raw 4DCBCT projection data-set. Deformation of the reference CT is accomplished via a patient specific motion model. The motion model is constrained spatially using eigenvectors generated by a principal component analysis (PCA) of patient motion data, and is regularized in time using parametric functions of a patient breathing surrogate recorded simultaneously with 4DCBCT acquisition. The parametric motion model is constrained using forward iterative projection matching (FIPM), a scheme which iteratively alters model parameters until digitally reconstructed radiographs (DRRs) cast through the deforming CT optimally match the projections in the raw 4DCBCT data-set. We term our method FIPM-PCA 4DCBCT. In developing our algorithm we proceed through three stages of development. In the first, we establish the mathematical groundwork for the algorithm and perform proof of concept testing on simulated data. In the second, we tune the algorithm for real world use; specifically we improve our DRR algorithm to achieve maximal realism by incorporating physical principles of image formation combined with empirical measurements of system properties. In the third stage we test our algorithm on actual patient data and evaluate its performance against gold standard and ground truth data-sets. In this phase we use our method to track the motion of an implanted fiducial marker and observe agreement with our gold standard data that is typically within a millimeter

Spline projection-based volume-to-image registration

This thesis focuses on the rigid-body registration of a three-dimensional model of an object to a set of its two-dimensional projections. The main contribution is the development of two registration algorithms that use a continuous model of the volume based on splines, either in the space domain or in the frequency domain. This allows for a well-defined gradient of the dissimilarity measure, which is a necessary condition for efficient and accurate registration. The first part of the thesis contains a review of the literature on volume-to- image registration. Then, we discuss data interpolation in the space domain and in the frequency domain. The basic concepts of our registration strategy are given in the second part of the thesis. We present a novel one-step approach for fast ray casting to simulate space-based volume projections. We also discuss the use of the central-slice theorem to simulate frequency-based volume projections. Then, we consider the question of the registration robustness. To improve the robustness of the space-based approach, we apply a multiresolution optimization strategy where spline-based data pyramids are processed in coarse-to-fine fashion, which improves speed as well. To improve the robustness of the frequency-based registration, we apply a coarse-to-fine strategy that involves weights in the frequency domain. In the third part, we apply our space-based algorithm to computer-assisted orthopedic surgery while adapting it to the perspective projection model. We show that the registration accuracy achieved using the orthopedic data is consistent with the current standards. Then, we apply our frequency-based registration to three-dimensional electron-microscopy application. We show that our algorithm can be used to obtain a refined solution with respect to currently available algorithms. The novelty of our approach is in dealing with a continuous space of geometric parameters, contrary to the standard methods which deal with quantized parameters. We conclude that our continuous parameter space leads to better registration accuracy. Last, we compare the performance of the frequency-based algorithm with that of the space-based algorithm in the context of electron microscopy. With these data, we observe that frequency-based registration algorithm outperforms the space-based one, which we attribute to the suitability of interpolation in the frequency domain when dealing with strictly space-limited data

Optimization of Operation Sequencing in CAPP Using Hybrid Genetic Algorithm and Simulated Annealing Approach

In any CAPP system, one of the most important process planning functions is selection of the operations and corresponding machines in order to generate the optimal operation sequence. In this paper, the hybrid GA-SA algorithm is used to solve this combinatorial optimization NP (Non-deterministic Polynomial) problem. The network representation is adopted to describe operation and sequencing flexibility in process planning and the mathematical model for process planning is described with the objective of minimizing the production time. Experimental results show effectiveness of the hybrid algorithm that, in comparison with the GA and SA standalone algorithms, gives optimal operation sequence with lesser computational time and lesser number of iterations

Optimization of Operation Sequencing in CAPP Using Hybrid Genetic Algorithm and Simulated Annealing Approach

In any CAPP system, one of the most important process planning functions is selection of the operations and corresponding machines in order to generate the optimal operation sequence. In this paper, the hybrid GA-SA algorithm is used to solve this combinatorial optimization NP (Non-deterministic Polynomial) problem. The network representation is adopted to describe operation and sequencing flexibility in process planning and the mathematical model for process planning is described with the objective of minimizing the production time. Experimental results show effectiveness of the hybrid algorithm that, in comparison with the GA and SA standalone algorithms, gives optimal operation sequence with lesser computational time and lesser number of iterations

Autonomous Navigation of Automated Guided Vehicle Using Monocular Camera

This paper presents a hybrid control algorithm for Automated Guided Vehicle (AGV) consisting of two independent control loops: Position Based Control (PBC) for global navigation within manufacturing environment and Image Based Visual Servoing (IBVS) for fine motions needed for accurate steering towards loading/unloading point. The proposed hybrid control separates the initial transportation task into global navigation towards the goal point, and fine motion from the goal point to the loading/unloading point. In this manner, the need for artificial landmarks or accurate map of the environment is bypassed. Initial experimental results show the usefulness of the proposed approach.COBISS.SR-ID 27383808