# Design of a Computed Tomography Automation Architecture
## 6 Computed Tomography Framework  
From the review of the literature, recent research has experimented with modifying the baseline steps of the CT procedure, displayed in Figure 2, and with the addition of new components into the process, through methods such as inputting prior information, feedback mechanisms, or post-imaging techniques. Recent research efforts represent  
Figure 6: Robot and cabinet CT system for automated loading and unloading in a manufacturing line. Ref. [45] Reprinted from CIRP Annals, Volume 63, Issue 2, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], Industrial applications of computed tomography, Pages 655–677, Copyright 2021, with permission from Elsevier. different ways of performing these baseline steps of the CT procedure, and this variability is necessary to apply to as many CT applications as possible. The diagram in Figure 7 is a general CT framework that includes the orientation, imaging, and reconstruction steps of the CT process. Additional blocks represent examples of additional methods that can be applied to the generic procedure based on the literature discussed in this paper. The image acquisition step of the CT process has several parameters that can be modified. Step size, number of projections, exposure time, source type, detector type, and the geometry of the source, object, and detector are some of the most important parameters. The framework that is discussed will identify and implement as many of the necessary image acquisition parameters as possible into its design. The 3D reconstruction step of the CT process changes based on the selection of an algorithm. There are a few reconstruction algorithms that are seen as most popular in the literature, but the framework will provide an opening for additional algorithms that are not included either due to the lack of documented applicability to current methods or because the algorithms have not been developed yet. Key cone-beam CT reconstruction algorithms are listed in Table 2. [PERSON]'s convolution backprojection is the most popular CT reconstruction method for cone-beam flat-panel detectors. In addition, some techniques in the literature go beyond modifying the way orientation, imaging, and reconstruction are performed. Examples of this include implementing existing information into the orientation decision process prior to any X-ray images being acquired, different physical orientation techniques with additional DOF, the use of more than one CT axis or non-circular trajectories, and adding feedback into the loop for deciding how to reorient the object. The creation of a flexible framework for industrial X-ray CT applications would help bring together contributions identified individually in the literature. The required components for performing CT, image acquisition, and 3D reconstruction, act as the core for this framework. These base components will offer customizable parameter decisions to  
Figure 7: Computed tomography framework with the generic CT approach (dark orange), along with additional extensions for certain applications that can be applied. Box colors: prior inputted data (blue), feedback loop (light orange), generic CT approach modifications (green), and post-processing analysis (black). Reference numbers are included as an example for the papers that focus on each aspect or method. apply to a broad set of applications. Additional techniques will act as add-ons to the base of this framework because its structure will define openings for add-ons to be implemented. This software framework integrates the individual components identified in the literature into a single framework compiled from the reviewed efforts. The core framework can be completed along with a new component addressing a key identified gap. This core framework with the newly developed capability can serve as an example for further opportunities to develop various application-specific solutions more easily. The framework also allows for other gaps identified in the literature to be addressed.