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Metal artefact reduction for accurate tumour delineation in radiotherapy.

ABSTRACT: 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?

SUBMITTER: Kovacs DG

PROVIDER: S-EPMC5864514 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Metal artefact reduction for accurate tumour delineation in radiotherapy.

Kovacs David Gergely DG Rechner Laura A LA Appelt Ane L AL Berthelsen Anne K AK Costa Junia C JC Friborg Jeppe J Persson Gitte F GF Bangsgaard Jens Peter JP Specht Lena L Aznar Marianne C MC

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology 20171016 3

<h4>Background and purpose</h4>Two techniques for metal artefact reduction for computed tomography were studied in order to identify their impact on tumour delineation in radiotherapy.<h4>Materials and methods</h4>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, ...[more]

PMID: 29050958

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Project description:ObjectivesThe susceptibility of CT imaging to metallic objects gives rise to strong streak artefacts and skewed information about the attenuation medium around the metallic implants. This metal-induced artefact in CT images leads to inaccurate attenuation correction in PET/CT imaging. This study investigates the potential of deep learning-based metal artefact reduction (MAR) in quantitative PET/CT imaging.MethodsDeep learning-based metal artefact reduction approaches were implemented in the image (DLI-MAR) and projection (DLP-MAR) domains. The proposed algorithms were quantitatively compared to the normalized MAR (NMAR) method using simulated and clinical studies. Eighty metal-free CT images were employed for simulation of metal artefact as well as training and evaluation of the aforementioned MAR approaches. Thirty 18F-FDG PET/CT images affected by the presence of metallic implants were retrospectively employed for clinical assessment of the MAR techniques.ResultsThe evaluation of MAR techniques on the simulation dataset demonstrated the superior performance of the DLI-MAR approach (structural similarity (SSIM) = 0.95 ± 0.2 compared to 0.94 ± 0.2 and 0.93 ± 0.3 obtained using DLP-MAR and NMAR, respectively) in minimizing metal artefacts in CT images. The presence of metallic artefacts in CT images or PET attenuation correction maps led to quantitative bias, image artefacts and under- and overestimation of scatter correction of PET images. The DLI-MAR technique led to a quantitative PET bias of 1.3 ± 3% compared to 10.5 ± 6% without MAR and 3.2 ± 0.5% achieved by NMAR.ConclusionThe DLI-MAR technique was able to reduce the adverse effects of metal artefacts on PET images through the generation of accurate attenuation maps from corrupted CT images.Key points• The presence of metallic objects, such as dental implants, gives rise to severe photon starvation, beam hardening and scattering, thus leading to adverse artefacts in reconstructed CT images. • The aim of this work is to develop and evaluate a deep learning-based MAR to improve CT-based attenuation and scatter correction in PET/CT imaging. • Deep learning-based MAR in the image (DLI-MAR) domain outperformed its counterpart implemented in the projection (DLP-MAR) domain. The DLI-MAR approach minimized the adverse impact of metal artefacts on whole-body PET images through generating accurate attenuation maps from corrupted CT images.

Project description:PurposeDelineation of tumour boundaries is important for quantification of [(18)F]fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET) studies and for definition of biological target volumes in radiotherapy. Several (semi-)automatic tumour delineation methods have been proposed, but these methods differ substantially in estimating tumour volume and their performance may be affected by imaging parameters. The main purpose of this study was to explore the performance dependence of various (semi-)automatic tumour delineation methods on different imaging parameters, i.e. reconstruction parameters, noise levels and tumour characteristics, and thereby the need for standardization or inter-institute calibration.MethodsSix different types of delineation methods were evaluated by assessing accuracy and precision in estimating tumour volume from simulations and phantom experiments. The evaluated conditions were various tumour sizes, iterative reconstruction algorithm settings and image filtering, tumour to background ratios (TBR), noise levels and region growing initializations.ResultsThe accuracy of all automatic delineation methods was influenced when imaging parameters were varied. The performance of all tumour delineation methods depends on variation of TBR, image resolution and image noise level, and to a lesser extent on number of iterations during image reconstruction or the initialization method of the region generation. For sphere sizes larger than 20 mm diameter a contrast-oriented method provided the most accurate results, on average, over all simulated conditions. For threshold-based methods the accuracy of tumour delineation improved after image denoising/filtering.ConclusionThe accuracy and precision of all studied tumour delineation methods was affected by physiological and imaging parameters. The latter illustrates the need for optimizing imaging parameters and/or for careful calibration and optimization of delineation methods.

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Metal artefact reduction for accurate tumour delineation in radiotherapy.

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Metal artefact reduction for accurate tumour delineation in radiotherapy.

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