학술논문

Evaluation of image quality of a novel computed tomography metal artifact management technique on an anthropomorphic head and neck phantom
Document Type
article
Source
Physics and Imaging in Radiation Oncology, Vol 17, Iss , Pp 111-116 (2021)
Subject
Computed X ray tomography
Artifacts
Head and neck neoplasms
Algorithm
Gantry tilts
Medical physics. Medical radiology. Nuclear medicine
R895-920
Neoplasms. Tumors. Oncology. Including cancer and carcinogens
RC254-282
Language
English
ISSN
2405-6316
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.