Metal artefact reduction algorithm for correction of bone biopsy needle artefact in paediatric C-arm CT images: a qualitative and quantitative assessment

2016 ◽  
Vol 71 (9) ◽  
pp. 925-931 ◽  
Author(s):  
S. Shellikeri ◽  
E. Girard ◽  
R. Setser ◽  
J. Bao ◽  
A.M. Cahill
Keyword(s):  
Quantitative Assessment ◽  
Bone Biopsy ◽  
Ct Images ◽  
Reduction Algorithm ◽  
Biopsy Needle ◽  
Qualitative And Quantitative ◽  
Metal Artefact Reduction ◽  
Metal Artefact ◽  
Artefact Reduction ◽  
Metal Artefact Reduction Algorithm

scholarly journals Localising ingested metallic foreign bodies to guide surgical planning: A novel use for the CT metal artefact reduction algorithm

2021 ◽  
pp. 20200189
Author(s):  
Peter Abernethy ◽  
Neil G McIntyre ◽  
Alexander Sanchez-Cabello ◽  
Ross Kruger ◽  
Dushyant Shetty
Keyword(s):  
Surgical Planning ◽  
Foreign Bodies ◽  
Surgical Intervention ◽  
Reduction Algorithm ◽  
Metal Artefact Reduction ◽  
Abdominal Radiographs ◽  
Metal Artefact ◽  
Artefact Reduction ◽  
Metal Artefact Reduction Algorithm ◽  
Joint Prostheses

We present the case of a 20 year old female patient who presented following ingestion of multiple button magnets. She remained clinically well however serial abdominal radiographs demonstrated the magnets were not passing through the gastrointestinal tract and a CT was therefore performed for further assessment and to aid surgical planning. Artefact from the magnets made interpretation of the CT challenging. The use of a Metal Artefact Reduction (MAR) algorithm however enabled accurate localisation of the magnets thus guiding subsequent surgical intervention. Whilst MAR algorithms are usually used in the assessment of iatrogenic metallic devices (e.g., joint prostheses), this case demonstrates an example of their potential wider use.

Prototype metal artefact reduction algorithm in flat panel computed tomography - evaluation in patients undergoing transarterial hepatic radioembolisation

2017 ◽  
Vol 28 (1) ◽  
pp. 265-273 ◽  
Author(s):  
Qeumars Mustafa Hamie ◽  
Adrian Raoul Kobe ◽  
Leif Mietzsch ◽  
Michael Manhart ◽  
Gilbert Dominique Puippe ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Reduction Algorithm ◽  
Flat Panel ◽  
Metal Artefact Reduction ◽  
Metal Artefact ◽  
Artefact Reduction ◽  
Metal Artefact Reduction Algorithm

scholarly journals Deep learning–based metal artefact reduction in PET/CT imaging

2021 ◽  
Author(s):  
Hossein Arabi ◽  
Habib Zaidi
Keyword(s):  
Deep Learning ◽  
Scatter Correction ◽  
Ct Images ◽  
Ct Imaging ◽  
Metal Artefact Reduction ◽  
Metal Artefacts ◽  
Quantitative Pet ◽  
Pet Ct ◽  
Metal Artefact ◽  
Artefact Reduction

Abstract Objectives The 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. Methods Deep 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. Results The 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. Conclusion The 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.

scholarly journals The Dosimetric Evaluation of An In-House Software Developed For Metal Artefact Reduction On Two Different Radiotherapy Treatment Planning Systems

2021 ◽  
Author(s):  
Aysun Inal ◽  
Songul Barlaz Us
Keyword(s):  
Treatment Planning ◽  
Dose Calculation ◽  
Hounsfield Unit ◽  
Ct Images ◽  
Planning Systems ◽  
Metal Artefact Reduction ◽  
Treatment Planning Systems ◽  
Gamma Evaluation ◽  
Metal Artefact ◽  
Artefact Reduction

Abstract Metal Artefact Reduction (MAR) is very important in terms of dose calculation in radiotherapy. It was aimed to develop an in-house MAR software as an alternative to commercial software programs and to examine its effectiveness by comparing it with the SMART-MAR software which was available commercially. A phantom containing metal with high atomic number was designed and computed tomography (CT) images of this phantom were taken (Without-MAR images). The obtained CT images were processed with the SMART-MAR software and the developed In-House MAR software. Processed images were compared in terms of Hounsfield unit (HU), absolute dose values ​​in the Accuray and CMS XiO treatment planning systems, and gamma evaluation. The best HU improvement was observed in the developed In-House MAR. The maximum mean percentage differences in absorbed doses at the determined points in Accuray was found 33.3% and 32.5% between Without MAR - SMART MAR and Without MAR- In-House MAR, respectively. The In-House MAR software developed by using MATLAB was shown similar results with the SMART MAR software. Although in-house MAR software needs to be investigated clinically, it is more advantageous than commercially available software in terms of being cost-free, applicability in a shorter time and without reconstruction.

Sign in / Sign up

Export Citation Format

Share Document