# Design of a Computed Tomography Automation Architecture
###### Abstract  
This paper presents a literature review on techniques related to the computed tomography procedure that incorporate automation elements in their research investigations or industrial applications. Computed tomography (CT) is a non-destructive testing (NDT) technique in that the imaging and inspection are performed without damaging the sample, allowing for additional or repeated analysis if necessary. The reviewed literature is organized based on the steps associated with a general NDT task in order to define an end-to-end computed tomography automation architecture. The process steps include activities prior to image collection, during the scan, and after the data are collected. It further reviews efforts related to repeating this process based on a previous scan result. By analyzing the multiple existing but disparate efforts found in the literature, we present a framework for fully automating NDT procedures and discuss the remaining technical gaps in the developed framework. computed tomography; radiography; automation; robotics; radiation imaging; X-ray 2021 18 March 2021 18 March 202  
grouped into parallel, fan, or cone beams (Figure 1). Detectors used for CT are exclusively digital, as analog methods can only be used for 2D imaging applications. An important step in any CT data collection process starts with determining how to orient the sample. It must be between the source and detector, but the distance from each and the angular orientation of the sample are variable. For CT, many images are taken at different angles, and therefore, an axis of rotation must be determined. The holding and transitioning of the sample between each angle are classically accomplished via a rotary stage. Thus, the staging platform limits the choice of axis due to its own kinematic constraints and potential to interfere with the image area. The imaging procedure is conducted by acquiring projections at small angular steps as the sample is rotated about its chosen axis. The step size and number of projections are chosen depending on the quality of CT data required, the amount of time allowed for the CT process, and the size of the part relative to the size of the detector. A smaller step size through 360\({}^{\circ}\) will help to improve 3D CT data quality but will add to the total processing time and size of the imaging data. These 2D projections are then put through a reconstruction algorithm to produce 3D CT data. This basic summary of the methodology and the simple procedure shown in Figure 2 hides several manual tasks necessary to generate a useful result. These manual tasks include, but are not limited to the following:  
* Deciding the position and orientation of the object (and detector);
* Selection of X-ray source energy and beam filtration;
* Precise placement of the object (and detector) in the chosen position and orientation;
* Reorientation and repositioning of the object between different scans;
* Post-processing and analysis (e.g., defect detection). Figure 1: Generic X-ray computed tomography (CT) process with cone-beam geometry. Reproduced, with permission, from Reference [3]. Manually completing these tasks can adversely affect the quality of the result, are time consuming, prone to error, and may require a repeated startup and shutdown of the radiation source due to the operator entering and exiting the imaging location. For these reasons, various efforts have been undertaken to automate aspects of CT. This review summarizes and synthesizes the published efforts that attempt to minimize these issues through automation. Much of the relevant research in this field focuses on accomplishing one of the following goals: defect inspection, artifact reduction, metrology, calibration, or achieving a faster or improved CT method. A literature review focusing on automation efforts enables us to synthesize the collective efforts into a complete architecture for automated CT.