About

Log in?

DTU users get better search results including licensed content and discounts on order fees.

Anyone can log in and get personalized features such as favorites, tags and feeds.

Log in as DTU user Log in as non-DTU user No thanks

DTU Findit

Journal article

Metal artefact reduction for accurate tumour delineation in radiotherapy

From

Department of Electrical Engineering, Technical University of Denmark1

Copenhagen University Hospital Herlev and Gentofte2

Background and purpose: Two techniques for metal artefact reduction for computed tomography were studied in order to identify their impact on tumour delineation in radiotherapy.Materials and methods: Using specially designed phantoms containing metal implants (dental, spine and hip) as well as patient images, we investigated the impact of two methods for metal artefact reduction on (A) the size and severity of metal artefacts and the accuracy of Hounsfield Unit (HU) representation, (B) the visual impact of metal artefacts on image quality and (C) delineation accuracy.

A metal artefact reduction algorithm (MAR) and two types of dual energy virtual monochromatic (DECT VM) reconstructions were used separately and in combination to identify the optimal technique for each implant site.Results: The artefact area and severity was reduced (by 48-76% and 58-79%, MAR and DECT VM respectively) and accurate Hounsfield-value representation was increased by 22-82%.

For each energy, the observers preferred MAR over non-MAR reconstructions (p <0.01 for dental and hip cases, p <0.05 for the spine case). In addition, DECT VM was preferred for spine implants (p <0.01). In all cases, techniques that improved target delineation significantly (p <0.05) were identified.Conclusions: DECT VM and MAR techniques improve delineation accuracy and the optimal of reconstruction technique depends on the type of metal implant. (C) 2017 The Authors.

Published by Elsevier Ireland Ltd. Radiotherapy and Oncology 126 (2018) 479-486 This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Language: English
Publisher: Elsevier Scientific Publishers
Year: 2018
Pages: 479-486
ISSN: 18790887 and 01678140
Types: Journal article
DOI: 10.1016/j.radonc.2017.09.029
ORCIDs: 0000-0002-3363-3256

DTU users get better search results including licensed content and discounts on order fees.

Log in as DTU user

Access

Analysis