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.

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

2021 ◽  
pp. 20200553
Author(s):  
Yuki Sakai ◽  
Erina Kitamoto ◽  
Kazutoshi Okamura ◽  
Masato Tatsumi ◽  
Takashi Shirasaka ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Oral Cavity ◽  
Image Noise ◽  
Ct Number ◽  
Metal Artefact Reduction ◽  
Metal Artefact ◽  
Artefact Reduction ◽  
The Impact ◽  
Better Than

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.

scholarly journals Quantitative Effect of Metal Artefact Reduction on CT-Based Attenuation Correction in FDG PET/CT in Patients With Hip Prosthesis

2020 ◽  
Author(s):  
Maarten Haemels ◽  
Delphine Vandendriessche ◽  
Jeroen De Geeter ◽  
James Velghe ◽  
Maxence Vandekerckhove ◽  
...  
Keyword(s):  
Hip Prosthesis ◽  
Hip Prostheses ◽  
Metal Artefact Reduction ◽  
Standardized Uptake Values ◽  
Pet Ct ◽  
Measured Activity ◽  
Metal Artefact ◽  
Artefact Reduction ◽  
Conventional Ct ◽  
Attenuation Corrected

Abstract Background Metal artefact reduction (MAR) techniques still are in limited use in positron emission tomography / computed tomography (PET/CT). This study aimed to investigate the effect of Smart MAR on quantitative PET analysis in the vicinity of hip prostheses.Material and methods Activities were measured on PET/CT images in 6 sources with 10-fold activity concentration contrast to background, attached to the head, neck and the major trochanter of a human cadaveric femur, and in the same sources in similar locations after a hip prosthesis (titanium cup, ceramic head, chrome-cobalt stem) had been inserted into the femur. Measurements were compared between PET attenuation corrected using either conventional or MAR CT. In 46 patients harbouring 61 hip prostheses, standardized uptake values (SUV) in 6 periprosthetic regions and the bladder, were compared between PET attenuation corrected with either conventional or MAR CT. Results Using conventional CT, measured activity decreased from 2 to 13% when the prosthesis was inserted. Use of MAR CT increased measured activity by up to 11 12% compared with conventional CT and reduced the relative difference with the reference values to under 5% in all sources.In all regions, to the exception of the prosthesis shaft, SUVmean increased significantly (p<0.001) by use of MAR CT. Median (interquartile range) percentual increases of SUVmean were 1.9 (0.0-4.5), 3.9 (1.8-7.8), 7.0 (3.4-11.1), 1.7 (0.9-3.7), 1.5 (0.8-3.3) in acetabulum, lateral neck, medial neck, lateral diaphysis and medial diaphysis, respectively. Except for the shaft, the coefficient of variation did not increase significantly. Except for the erratic changes in the prosthesis shaft, decreases of SUVmean were rare and small. Bladder SUVmean increased by 1% in patients with unilateral and by 4% in patients with bilateral prosthesis. Conclusions In a realistic hip prosthesis phantom, Smart MAR restores quantitative accuracy by recovering counts in underestimated sources. In patient studies, Smart MAR increases SUV in all areas surrounding the prosthesis, most markedly in the femoral neck region. This proves that underestimation of activity is the most prevalent metal artefact in hip prostheses. Smart MAR increases SUV in the urinary bladder, indicating effects at a distance from the prosthesis.

scholarly journals Metal artefact reduction in CT imaging of hip prostheses—an evaluation of commercial techniques provided by four vendors

2015 ◽  
Vol 88 (1052) ◽  
pp. 20140473 ◽  
Author(s):  
K M Andersson ◽  
P Nowik ◽  
J Persliden ◽  
P Thunberg ◽  
E Norrman
Keyword(s):  
Ct Imaging ◽  
Hip Prostheses ◽  
Metal Artefact Reduction ◽  
Metal Artefact ◽  
Artefact Reduction

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.

Combined dual energy and iterative metal artefact reduction for PET/CT in head and neck cancer

2020 ◽  
Vol 65 (24) ◽  
pp. 245010
Author(s):  
D G Kovacs ◽  
C N Ladefoged ◽  
A K Berthelsen ◽  
B M Fischer ◽  
F L Andersen
Keyword(s):  
Head And Neck Cancer ◽  
Head And Neck ◽  
Neck Cancer ◽  
Dual Energy ◽  
Metal Artefact Reduction ◽  
Pet Ct ◽  
Metal Artefact ◽  
Artefact Reduction
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