TECHNICAL NOTEIterative Ring Artifact Removal Method for Helical Computed Tomography ScansBalogh, Zsolt Adam PhD∗; Huszar, Tamas MA†; Kis, Benedek Janos MA†Author Information From the ∗Department of Mathematical Sciences, College of Science, United Arab Emirates University, Al Ain, United Arab Emirates †Mediso Ltd., Budapest, Hungary. Received for publication March 3, 2020; accepted June 2, 2020. Correspondence to: Zsolt Adam Balogh, PhD, Department of Mathematical Sciences, College of Science, United Arab Emirates University, Al Ain, United Arab Emirates (e-mail: firstname.lastname@example.org). The authors declare no conflict of interest. Journal of Computer Assisted Tomography: 9/10 2020 - Volume 44 - Issue 5 - p 796-805 doi: 10.1097/RCT.0000000000001070 Buy Metrics Abstract Objective In this article, a statistical-based iterative ring removal (IRR) algorithm that effectively removes ring artifacts generated by defective detector cells is proposed. Methods The physical state of computed tomography (CT) detector elements can change dynamically owing to their temperature dependence and the varying irradiation caused by focal spot movements. This variation in the properties of cells may cause false pixel values in sinograms, resulting in rings or segments of rings in reconstructed images. In this article, the proposed algorithm is studied on clinical CT. Two patients were scanned using a clinical CT scanner (AnyScan SPECT/CT, Mediso). Artificial rings and band rings were generated on the real sinogram data to examine the algorithm in different cases. The method was performed also on real ring artifacts. Results The IRR can correct both single and band-like ring artifacts with one or more defective pixels. The proposed algorithm can detect the period when pixels contain false signals and only those periods are corrected. The IRR reduces ring artifacts, even in cases where low-contrast rings occur in the reconstructed image. Conclusions This statistical correction method efficiently detects and corrects false pixel values in the projection data without causing new artifacts in the reconstructed image. The algorithm is less sensitive to its parameters. Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.