Complex artefact suppression for sparse reconstruction based on compensation approach in X‐ray computed tomography. Issue 4 (18th December 2022)

• Record Type: Journal Article
• Authors: Yang, Fuqiang; Zhang, Dinghua; Huang, Kuidong; Yang, Yao; Li, Zhixiang
• Is Part Of: IET image processing. Volume 17:Issue 4(2023)
• Page Start: 1291
• Page End: 1306
• Publication Date: 2022-12-18
• Subjects: computed tomography -- image reconstruction -- non‐destructive testing -- X‐ray imaging; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/ipr2.12713
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract: To provide high‐quality imaging by decreasing the sparse view imaging artefact from the computed tomography (CT) images, this study addresses a new artefact suppression technique for sparse data for both single material and multi‐material objects that have diverse materials. It begins with a pre‐reconstructed image and a network of target patches that have been trained beforehand and then uses the forward projection (FP) approach to resolve the structural mutation brought on by sparse‐view projection. As an edge‐preserving operator to commit to the forward operator for sinogram correction, the bilateral filter was used. Both simulated and actual data have been gathered and evaluated in experiments. The suggested forward operator and normalized compensation (FONC) method produce results that have far smaller artefact and errors than those of more traditional techniques. For simulation # blade, the Normalized Mean Square Distance (NMSD) of the proposed method was reduced by 8.94%, Structural Similarity Index (SSIM) and the Universal Quality Index (UQI) were increased by 78.17% and 80.49%, respectively, which also demonstrate better uniformity of the results to practical data # pan, where the root mean squared error was reduced by 13.93%. SSIM and UQI were increased by 25.66% and 37.02%, respectively. The results conclusively show that the planned strategies are successful in eliminating artefact for irregular objects. Abstract : This paper presents an artefacts suppression for artificial and industrial objects with sparse data in computed tomography (CT). It starts by a pre‐reconstructed image and the pre‐trained network of target patches, and follows by the forward projection (FP) method to make up the structural mutation caused from the ill‐posed projection. Since the preliminary image has been employed as the knowledge, the bilateral filter was applied as an edge‐preserving operator to commit to the forward operator for sinogram compensation.