Optimization of Computer Tomography Reconstruction Parameters using Additive Manufacturing

Abstract

Additive Manufacturing (AM) is one of the advanced engineering manufacturing process and the application of this process is entered into each and every industry. This process best suits for production of each part uniquely. This technology best fits for medical and dental industry, where each patient has unique anatomy. Cone Beam Computed Tomography (CBCT), Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are the major input data source for the AM medical software's. The medical data is usually stored in Digital Imaging and Communication in Medicine (DICOM) file format. In the current days the most of the CT scanners are of multi slice scanners, which help to acquire maximum data of patient anatomy with in minimum time. Once CT data acquisition is done the reconstruction of data will start. In reconstruction of CT data slice thickness, slice increment and field of view parameters play's major role. The current work is to obtain best quality of data with minimal errors by optimizing the reconstruction parameters. Considered three reconstruction parameters with three levels to conduct the experiments. The reconstruction data is analyzed using L9 orthogonal array and S/N (Signal to Noise) ratio. The paper also explains the importance of reconstruction parameters theoretically and validated by experimental analysis, also applied on few case studies. The experimental results prove that slice thickness is majorly responsible for the quality of reconstructed data. The dimensional error is reduced from 0.78 mm to 0.65 mm. The same optimal parameters are implemented in the two case studies.