Detection model modeling and application for batch scans of Cone-Beam Computed Tomography

摘要

Aiming at the artifact corrections for batch scans in Cone-Beam Computed Tomography (CBCT) system, the concept of detection model is proposed. Expressing the prior knowledge of CT system and scanned object properties by the detection model, the rapid artifact corrections are achieved based on the object knowledge, which can save the machine consumption, enhance the detection efficiency and improve the correction effect. Based on the Digital Radiography (DR) imaging conditions remained basically unchanged in the batch scans, the modeling method of detection model is established by getting the relevant information through the detected scanning for one of a batch of parts. Finally, the processing flow of CBCT scans and artifact corrections of a batch of parts based on the detection model is given, and some key problems in the flow are discussed to improve the practical operability of the method. The experimental result shows that the modeling method of detection model is feasible, and the rapid CBCT scans and effective artifact corrections can be realized based on the obtained detection model.