Metal artefact reduction in the oral cavity using deep learning reconstruction algorithm in ultra-high-resolution computed tomography: a phantom study

Objectives: This study aimed to improve the impact of the metal artefact reduction (MAR) algorithm for the oral cavity by assessing the effect of acquisition and reconstruction parameters on an ultra-high-resolution CT (UHRCT) scanner. Methods: The mandible tooth phantom with and without the lesion was scanned using super-high-resolution, high-resolution (HR), and normal-resolution (NR) modes. Images were reconstructed with deep learning-based reconstruction (DLR) and hybrid iterative reconstruction (HIR) using the MAR algorithm. Two dental radiologists independently graded the degree of metal artefact (1, very severe; 5, minimum) and lesion shape reproducibility (1, slight; 5, almost perfect). The signal-to-artefact ratio (SAR), accuracy of the CT number of the lesion, and image noise were calculated quantitatively. The Tukey-Kramer method with a p-value of less than 0.05 was used to determine statistical significance. Results: The HRDLR visual score was better than the NRHIR score in terms of degree of metal artefact (4.6 ± 0.5 and 2.6 ± 0.5, p < 0.0001) and lesion shape reproducibility (4.5 ± 0.5 and 2.9 ± 1.1, p = 0.0005). The SAR of HRDLR was significantly better than that of NRHIR (4.9 ± 0.4 and 2.1 ± 0.2, p < 0.0001), and the absolute percentage error of the CT number in HRDLR was lower than that in NRHIR (0.8% in HRDLR and 23.8% in NRIR). The image noise of HRDLR was lower than that of NRHIR (15.7 ± 1.4 and 51.6 ± 15.3, p < 0.0001). Conclusions: Our study demonstrated that the combination of HR mode and DLR in UHRCT scanner improved the impact of the MAR algorithm in the oral cavity.

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Combination of high-resolution cone beam computed tomography and metal artefact reduction software: a new image fusion technique for evaluating intracranial stent apposition after aneurysm treatment

BMJ Case Reports ◽

10.1136/bcr-2019-230687 ◽

2019 ◽

Vol 12 (9) ◽

pp. e230687 ◽

Cited By ~ 2

Author(s):

Ichiro Yuki ◽

Toshihiro Ishibashi ◽

Chihebeddine Dahmani ◽

Naoki Kato ◽

Ayako Ikemura ◽

...

Keyword(s):

High Resolution ◽

Image Fusion ◽

Imaging Technique ◽

Three Dimensional ◽

Cone Beam ◽

Brain Aneurysm ◽

Metal Artefact Reduction ◽

Aneurysm Treatment ◽

Metal Artefact ◽

Artefact Reduction

We introduce a new imaging technique to improve visualisation of stent apposition after endovascular treatment of brain aneurysms employing high-resolution cone beam CT and three-dimensional digital subtraction angiography. After performing a stent-assisted coil embolisation of brain aneurysm, the image datasets were processed with a metal artefact reduction software followed by the automated image fusion programmes. Two patients who underwent aneurysm coiling using a Neuroform stent were evaluated. The reconstructed 3D images showed a detailed structure of the stent struts and identified malappositions of the deployed stents. Case 1 showed good apposition on the outer curvature side of the carotid siphon, while the inner curvature side showed prominent malapposition. Case 2, with multiple aneurysms, showed good apposition on both outer and inner curvature sides, although inward prolapse of the struts was observed. This new imaging technique may help evaluate stent apposition after the endovascular aneurysm treatment.

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Combining Multimodal Information for Metal Artefact Reduction: An Unsupervised Deep Learning Framework

2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) ◽

10.1109/isbi45749.2020.9098633 ◽

2020 ◽

Author(s):

Marta B.M. Ranzini ◽

Irme Groothuis ◽

Kerstin Klaser ◽

M. Jorge Cardoso ◽

Johann Henckel ◽

...

Keyword(s):

Deep Learning ◽

Metal Artefact Reduction ◽

Learning Framework ◽

Multimodal Information ◽

Unsupervised Deep Learning ◽

Metal Artefact ◽

Artefact Reduction

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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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