Evaluation of image quality of a novel computed tomography metal artifact management technique on an anthropomorphic head and neck phantom

Daniela Branco; Stephen Kry; Paige Taylor; John Rong; Xiaodong Zhang; Steven Frank; David Followill

doi:10.1016/j.phro.2021.01.007

Evaluation of image quality of a novel computed tomography metal artifact management technique on an anthropomorphic head and neck phantom

Phys Imaging Radiat Oncol. 2021 Feb 21:17:111-116. doi: 10.1016/j.phro.2021.01.007. eCollection 2021 Jan.

Authors

Daniela Branco¹, Stephen Kry¹, Paige Taylor¹, John Rong², Xiaodong Zhang¹, Steven Frank¹, David Followill¹

Affiliations

¹ Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Unit 607, Houston, TX 77030, United States.
² Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Unit 607, Houston, TX 77030, United States.

Abstract

Background and purpose: Artefacts caused by dental amalgam implants present a common challenge in computed tomography (CT) and therefore treatment planning dose calculations. The goal was to perform a quantitative image quality analysis of our Artifact Management for Proton Planning (AMPP) algorithm which used gantry tilts for managing metal artefacts on Head and Neck (HN) CT scans and major vendors' commercial approaches.

Materials and methods: Metal artefact reduction (MAR) algorithms were evaluated using an anthropomorphic phantom with a removable jaw for the acquisition of images with and without (baseline) metal artifacts. AMPP made use of two angled CT scans to generate one artifact-reduced image set. The MAR algorithms from four vendors were applied to the images with artefacts and the analysis was performed with respective baselines. Planar HU difference maps and volumetric HU differences were analyzed.

Results: AMPP algorithm outperformed all vendors' commercial approaches in the elimination of artefacts in the oropharyngeal region, showing the lowest percent of pixels outside +- 20 HU criteria, 4%; whereas those in the MAR-corrected images ranged from 26% to 67%. In the region of interest within the affected slices, the commercial MAR algorithms showed inconsistent performance, whereas the AMPP algorithm performed consistently well throughout the phantom's posterior region.

Conclusions: A novel MAR algorithm was evaluated and compared to four commercial algorithms using an anthropomorphic phantom. Unanimously, the analysis showed the AMPP algorithm outperformed vendors' commercial approaches, showing the potential to be broadly implemented, improve visualizations in patient anatomy and provide accurate HU information.

Keywords: AMPP, Artifact Management for Proton Planning; Algorithm; Artifacts; CT, Computed tomography; Computed X ray tomography; Gantry tilts; HU, Hounsfield Unit; Head and neck neoplasms; MAR, metal artifact reduction; OAR, Organs at Risk; OMAR, orthopedic metal artifact reduction; SEMAR, single-energy metal artifact reduction; SmartMAR, Smart metal artifact reduction; iMAR, iterative metal artifact reduction; kVp, Kilovoltage peak.