Metal artefact correction algorithm based-on DSAT technique for CT images

Objective: The aim of the current study is to evaluate the effectiveness of reduction metal artifacts using kV-CT image with the single-energy based metal artefact reduction (SEMAR) technique by single-energy reconstruction, monochromatic CT and rED reconstructed by dual-energy reconstruction. Methods: Seven different metal materials (brass, aluminum, copper, stainless, steel, lead and titanium) were placed inside the water-based PMMA phantom. After DECT-based scan, the artefact index (AI) were evaluated with the kV-CT images with and without SEMAR by single-energy reconstruction, and raw-data based electron density (rED), monochromatic CT images by dual-energy reconstruction. Moreover, the AI with evaluated with rED and the converted ED images from the kV-CT and monochromatic CT images. Results: The minimum average value of the AI with all-metal inserts was approximately 80 keV. The AI without SEMAR was larger than that with SEMAR for the 80 kV and 135 kV CT images. In the comparison of the AI for the rED and ED images that were converted from 80 kV and 135 kV CT images with and without SEMAR, the monochromatic CT images of the PMMA phantom with inserted metal materials at 80 keV revealed that the kV-CT with SEMAR reduced the metal artefact substantially. Conclusion: The converted ED from the kV-CT and monochromatic CT images could be useful for a comparison of the AI using the same contrast scale. The kV-CT image with SEMAR by single-energy reconstruction was found to substantially reduce metal artefact. Advances in knowledge: The effectiveness of reduction of metal artifacts using single-energy based metal artefact reduction (SEMAR) technique and dual-energy CT (DECT) was evaluated the electron density conversion techniques.

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Shading correction algorithm for improvement of cone-beam CT images in radiotherapy

Physics in Medicine and Biology ◽

10.1088/0031-9155/53/20/010 ◽

2008 ◽

Vol 53 (20) ◽

pp. 5719-5733 ◽

Cited By ~ 39

Author(s):

T E Marchant ◽

C J Moore ◽

C G Rowbottom ◽

R I MacKay ◽

P C Williams

Keyword(s):

Cone Beam Ct ◽

Ct Images ◽

Cone Beam ◽

Correction Algorithm

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Unicompartmental knee arthroplasty MRI: impact of slice-encoding for metal artefact correction MRI on image quality, findings and therapy decision

European Radiology ◽

10.1007/s00330-015-3596-4 ◽

2015 ◽

Vol 25 (7) ◽

pp. 2184-2193 ◽

Cited By ~ 10

Author(s):

Christoph A. Agten ◽

Filippo Del Grande ◽

Sandro F. Fucentese ◽

Samuel Blatter ◽

Christian W. A. Pfirrmann ◽

...

Keyword(s):

Image Quality ◽

Knee Arthroplasty ◽

Unicompartmental Knee Arthroplasty ◽

Therapy Decision ◽

Unicompartmental Knee ◽

Metal Artefact ◽

Artefact Correction

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

European Radiology ◽

10.1007/s00330-021-07709-z ◽

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.

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CURVATURE-BASED CORRECTION ALGORITHM FOR AUTOMATIC LUNG SEGMENTATION ON CHEST CT IMAGES

Journal of Biological System ◽

10.1142/s0218339014500016 ◽

2014 ◽

Vol 22 (01) ◽

pp. 1-28 ◽

Cited By ~ 2

Author(s):

SHICHENG HU ◽

KESEN BI ◽

QUANXU GE ◽

MINGCHAO LI ◽

XIN XIE ◽

...

Keyword(s):

Ct Images ◽

Chest Ct ◽

Smoothing Algorithm ◽

Correction Algorithm ◽

Lung Segmentation ◽

Preliminary Step ◽

Chest Computed Tomography ◽

Optimal Values ◽

Selection Of

In order to ameliorate the lung defects caused by missed juxtapleural nodules in lung segmentation on chest computed tomography (CT) images, we develop a Newton–Cotes-based smoothing algorithm (NCBS) which is used as a preliminary step to remove noises as many as possible. Next considering the crescent outline features of the lung, we propose a curvature-based correction algorithm (CBC) for the determination of the correction threshold. The application of the proposed algorithms is demonstrated in the process of lung segmentation and the experimental results on 25 real datasets are illustrated. Furthermore, some experiments are conducted to investigate the effects of the key parameters in CBC on the performances of lung segmentation so as to decide their optimal values. In addition, the CBC is compared with other methods analytically and experimentally. The overall results show that our proposed algorithm in lung segmentation excels the related methods on the capability of automatic selection of the correction threshold, as well as the performances of accuracy, efficiency and feasibility.

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

10.21203/rs.3.rs-383784/v1 ◽

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.

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