scholarly journals CT Metal Artifact Reduction based on Virtual Generated Artifacts Using Modified pix2pix

2020 ◽  
Author(s):  
Xie Kai ◽  
Gao Liugang ◽  
Lu Zhengda ◽  
Li Chunying ◽  
Lin Tao ◽  
...  
Keyword(s):  
Dose Calculation ◽  
Ct Images ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Vertebral Metastases ◽  
Metal Artifacts ◽  
Reference Images ◽  
Spinal Brace ◽  
The Mean

Abstract Background: Metal artifacts introduce challenges in image-guided diagnosis or accurate dose calculations. This study aims to reduce metal artifacts from the spinal brace by using virtual generated artifacts through convolutional neural networks and to compare the performance of this approach with two other methods, namely, linear interpolation metal artifact reduction (LIMAR) and normalized metal artifact reduction (NMAR) .Method: A total of 3,600-slice CT images of 60 vertebral metastases patients were selected. The spinal cord center was marked in each image, metal masks were added to two sides of the marker to generate artifact-insert CT images, and the CT values of the metal parts were copied to original CT images to obtain reference CT images. These images were divided into training (3,000 slices) and test (600) sets. The modified U-Net and pix2pix architecture was applied to understand the relationship between the reference and artifact-insert images. The mean absolute error (MAE), mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) were calculated between the reference CT images and the predicted CT through LIMAR, NMAR, U-Net, and pix2pix. The CT values of organs from different images were compared. Radiotherapy treatment plans for vertebral metastases were designed, and dose calculation was performed. The dose distribution in different types of images was also compared.Results: The MAE values between the reference images and those images generated by LIMAR, NMAR, U-Net, and pix2pix were 15.02, 16.16, 6.12, and 6.48 HU, respectively, and the corresponding PSNR values were 15.37, 152.70, 158.93, and 65.14 dB, respectively. Pix2pix restored more texture than U-Net according to the visual comparison. The average CT values in the artifact-insert images of the liver, spleen, and left and right kidneys were all significantly higher than those of the reference images (p<0.05). The average CT values of the organs in images processed by the four methods showed no significant differences from those of the organs in the reference images. The mean dose of planned target volume in the artifact-insert images was significantly lower than that in the reference CT images. The average γ passing rate (1%, 1 mm) of the artifact-insert images was significantly lower than that of the reference images (95.9±1.4% vs. 99.2±1.4%, p<0.05).Conclusions: U-Net and pix2pix deep learning networks can remarkably reduce metal artifacts and improve critical structure visualization compared with LIMAR and NMAR according to the simulation data of artifact-insert images in the spinal brace. Pix2pix can restore more texture with the help of a discriminator. Metal artifacts increase the dose calculation uncertainty in radiotherapy. The dose calculated through images obtained by U-Net and pix2pix was identical with that calculated through reference images.

scholarly journals Evaluation of the quality of CT images acquired with smart metal artifact reduction software

2018 ◽  
Vol 13 (1) ◽  
pp. 155-162 ◽  
Author(s):  
Peng Zhou ◽  
Chunling Zhang ◽  
Zhen Gao ◽  
Wangshu Cai ◽  
Deyue Yan ◽  
...  
Keyword(s):  
Image Quality ◽  
Quantitative Assessment ◽  
Objective Assessment ◽  
Ct Images ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Metal Artifacts ◽  
Embolization Coil

AbstractObjectiveTo evaluate the practical effectiveness of smart metal artifact reduction (SMAR) in reducing artifacts caused by metallic implants.MethodsPatients with metal implants underwent computed tomography (CT) examinations on high definition CT scanner, and the data were reconstructed with adaptive statistical iterative reconstruction (ASiR) with value weighted to 40% and smart metal artifact reduction (SMAR) technology. The comparison was assessed by both subjective and objective assessment between the two groups of images. In terms of subjective assessment, three radiologists evaluated image quality and assigned a score for visualization of anatomic structures in the critical areas of interest. Objectively, the absolute CT value of the difference (ΔCT) and artifacts index (AI) were adopted in this study for the quantitative assessment of metal artifacts.ResultsIn subjective image quality assessment, three radiologists scored SMAR images higher than 40% ASiR images (P<0.01) and the result suggested that visualization of critical anatomic structures around the region of the metal object was significantly improved by using SMAR compared with 40% ASiR. The ΔCT and AI for quantitative assessment of metal artifacts showed that SMAR appeared to be superior for reducing metal artifacts (P<0.05) and indicated that this technical approach was more effective in improving the quality of CT images.ConclusionA variety of hardware (dental filling, embolization coil, instrumented spine, hip implant, knee implant) are processed with the SMAR algorithm to demonstrate good recovery of soft tissue around the metal. This artifact reduction allows for the clearer visualization of structures hidden underneath.

Metal artifact reduction in CT, a phantom study: subjective and objective evaluation of four commercial metal artifact reduction algorithms when used on three different orthopedic metal implants

2018 ◽  
Vol 59 (9) ◽  
pp. 1110-1118 ◽  
Author(s):  
Kirsten Bolstad ◽  
Silje Flatabø ◽  
Daniel Aadnevik ◽  
Ingvild Dalehaug ◽  
Nils Vetti
Keyword(s):  
Subjective Evaluation ◽  
Ct Images ◽  
Objective Analysis ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Metal Artifacts ◽  
Ct Number ◽  
Metal Implants ◽  
Cobalt Chrome

Background Metal implants may introduce severe artifacts in computed tomography (CT) images. Over the last few years dedicated algorithms have been developed in order to reduce metal artifacts in CT images. Purpose To investigate and compare metal artifact reduction algorithms (MARs) from four different CT vendors when imaging three different orthopedic metal implants. Material and Methods Three clinical metal implants were attached to the leg of an anthropomorphic phantom: cobalt-chrome; stainless steel; and titanium. Four commercial MARs were investigated: SmartMAR (GE); O-MAR (Philips); iMAR (Siemens); and SEMAR (Toshiba). The images were evaluated subjectively by three observers and analyzed objectively by calculating the fraction of pixels with CT number above 500 HU in a region of interest around the metal. The average CT number and image noise were also measured. Results Both subjective evaluation and objective analysis showed that MARs reduced metal artifacts and improved the image quality for CT images containing metal implants of steel and cobalt-chrome. When using MARs on titanium, all MARs introduced new visible artifacts. Conclusion The effect of MARs varied between CT vendors and different metal implants used in orthopedic surgery. Both in subjective evaluation and objective analysis the effect of applying MARs was most obvious on steel and cobalt-chrome implants when using SEMAR from Toshiba followed by SmartMAR from GE. However, MARs may also introduce new image artifacts especially when used on titanium implants. Therefore, it is important to reconstruct all CT images containing metal with and without MARs.

A metal artifact reduction scheme in CT by a Poisson fusion sinogram based postprocessing method

2021 ◽  
pp. 1-13
Author(s):  
Hui Tang ◽  
Yu Bing Lin ◽  
Guo Yan Sun ◽  
Xu Dong Bao
Keyword(s):  
Image Quality ◽  
Ct Images ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Metal Artifacts ◽  
Visual Evaluation ◽  
Clinical Images ◽  
Metal Free ◽  
Simulated Images

OBJECTIVE: To reduce metal artifacts generated using current interpolation-based method on X-ray computed tomography (CT) images, this study proposes and tests a new Poisson fusion sinogram based metal artifact reduction (FS-MAR) method. METHODS: The proposed FS-MAR method consists of (1) generating the prior image, (2) forward projecting this prior image and applying the Poisson blending technique to seamlessly replace the metal-affected sinogram of the original projection in the metal projection region (MPR) by the prior image projection to get the corrected metal-free sinogram, and (3) performing the filtered back projection (FBP) on the corrected sinogram and filling the metal image back to the metal-free corrected image to get the final artifact reduced image. Simulated images are calculated by taking clinical metal-free CT images as phantoms and inserting metals during the simulated projection process to get the corresponding metal-affected images by the FBP. After the simulated images are processed by the proposed MAR method, two metrics structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) are used to evaluate image quality. Finally, visual evaluation is also performed using several real clinical metal-affected images obtained from the Revision Radiology group. RESULTS: In two testing samples, using FS-MAR method yields the highest SSIM and PSNR of 0.8912 and 30.6693, respectively. Visual evaluation results on both simulated and clinical images also show that using FS-MAR method generates less image artifacts than using the interpolation-based algorithm. CONCLUSIONS: This study demonstrated that with the same prior image, applying the proposed Poisson FS-MAR method can achieve the higher image quality than using the interpolation-based algorithm.

scholarly journals CT Dental Artifact: Comparison of an Iterative Metal Artifact Reduction Technique with Weighted Filtered Back-Projection

2017 ◽  
Vol 6 (11) ◽  
pp. 205846011774327 ◽  
Author(s):  
Felix E Diehn ◽  
Gregory J Michalak ◽  
David R DeLone ◽  
Amy L Kotsenas ◽  
E Paul Lindell ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Skin Surface ◽  
Ct Images ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Imaging Features ◽  
Metal Artifact Reduction ◽  
Back Projection ◽  
Metal Artifacts ◽  
Filtered Back Projection

Background Dental hardware produces streak artifacts on computed tomography (CT) images reconstructed with the standard weighted filtered back projection (wFBP) method. Purpose To perform a preliminary evaluation of an iterative metal artifact reduction (IMAR) technique to assess its ability to improve anatomic visualization over wFBP in patients with dental amalgam or other hardware. Material and Methods CT images from patients with dental hardware were reconstructed using wFBP and IMAR software and soft-tissue or bone window/level settings. The anatomy most affected by metal artifacts was identified. Two neuroradiologists determined subjective and objective imaging features, including overall metal artifact score (1 = severe artifacts, 5 = no artifacts), soft-tissue visualization score of the most-compromised structure, and artifact length along the skin surface. CT numbers were used to quantify artifact severity. Results Twenty-four patients were included. IMAR improved overall metal artifact score in 18/24 cases (median =2 ± 0.9 vs. 1 ± 0.6, P < 0.001). Mean CT number in the most-affected anatomical structure significantly improved with IMAR (94.6 vs. 219 HU, P = 0.002) and length of affected skin surface decreased (40.4 mm vs. 118.7 mm, P < 0.001). However, osseous/dental artifactual defects were found in 22/24 cases with IMAR vs. 11/24 with wFBP. Conclusion IMAR software reduced metal artifact both subjectively and objectively and improved visualization of adjacent soft tissues. However, it produced a higher rate of artifactual defects in the teeth and bones than wFBP. Our findings support the use of IMAR as a valuable complement to, but not a replacement for, standard wFBP image reconstruction.

scholarly journals MR Image Based Approach for Metal Artifact Reduction in X-Ray CT

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Andras Anderla ◽  
Dubravko Culibrk ◽  
Gaspar Delso ◽  
Milan Mirkovic
Keyword(s):  
Linear Interpolation ◽  
Ct Images ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Metal Artifacts ◽  
Mr Image ◽  
Dental Fillings ◽  
Anatomical Information

For decades, computed tomography (CT) images have been widely used to discover valuable anatomical information. Metallic implants such as dental fillings cause severe streaking artifacts which significantly degrade the quality of CT images. In this paper, we propose a new method for metal-artifact reduction using complementary magnetic resonance (MR) images. The method exploits the possibilities which arise from the use of emergent trimodality systems. The proposed algorithm corrects reconstructed CT images. The projected data which is affected by dental fillings is detected and the missing projections are replaced with data obtained from a corresponding MR image. A simulation study was conducted in order to compare the reconstructed images with images reconstructed through linear interpolation, which is a common metal-artifact reduction technique. The results show that the proposed method is successful in reducing severe metal artifacts without introducing significant amount of secondary artifacts.

Segmentation-aided adaptive filtering for metal artifact reduction in radio-therapeutic CT images

2004 ◽  
Author(s):  
Celine Saint Olive ◽  
Michael R. Kaus ◽  
Vladimir Pekar ◽  
Kai Eck ◽  
Lothar Spies
Keyword(s):  
Adaptive Filtering ◽  
Ct Images ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction

scholarly journals Evaluation of prosthetic complications and tumor recurrence with the single energy metal artifact reduction algorithm in patients with hip tumor prostheses

2020 ◽  
Author(s):  
Fangling Zhang ◽  
Xiaoling Zhang ◽  
Ling Ma ◽  
Ruocheng Li ◽  
Zhaohui Zhang ◽  
...  
Keyword(s):  
Tumor Recurrence ◽  
Ordinal Scale ◽  
Rank Test ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Metal Artifacts ◽  
Signed Rank Test ◽  
Ct Attenuation Value ◽  
Prosthetic Complications

Abstract Background: To evaluate the effectiveness of the single energy metal artifact reduction (SEMAR) algorithm with a second-generation 320-row multi-detector computed tomography (MDCT) on complications and tumor recurrence detection in patients with hip tumor prostheses.Methods: From February 2016 to June 2019, 31consecutive patients with tumor prostheses of the hip joint underwent CT scans. Lesions were confirmed by histology or clinical and imaging follow-up. Images were reconstructed using 2 methods: iterative (IR) algorithm alone and SEMAR algorithm (IR+ SEMAR). Two radiologists graded the image quality visually by a 6-point (from 0 to 5) ordinal scale. Standard deviations (SD) of CT attenuation value defined as the artifact index (AI) were compared between the two reconstructed methods. Paired sample t-test was adopted to compare the AI values on IR and SEMAR images. Wilcoxon matched-pairs signed rank test was performed to compare the visual scores on IR and SEMAR images. A p- value less than 0.05 was considered statistically significant. Results: The artifacts of the SEMAR images were reduced compared to the Non-SEMAR images (113.94 ±128.54 vs 35.98 ± 53.75HU，t=2.867, P < 0.05). 20 and 16 more lesions were detected by observer 1 and observer 2 with SEMAR algorithm respectively. The mean scores of lesions without SEMAR were 1.39 ± 1.45 (observer 1) and 1.55± 1.34 (observer 2); with SEMAR, the scores were significantly higher, 4.42±0.56 (z=-4.752, p < 0.001) and 4.54± 0.72 (z=-4.837, p < 0.001) respectively. Conclusion: The SEMAR algorithm can effectively reduce metal artifacts in patients with hip tumor prostheses and increase the diagnostic accuracy of prosthetic complications and tumor recurrence.

Sign in / Sign up

Export Citation Format

Share Document