scholarly journals A projection-domain iterative algorithm for metal artifact reduction by minimizing the total-variation norm and the negative-pixel energy

2022 ◽  
Vol 5 (1) ◽  
Author(s):  
Gengsheng L. Zeng
Keyword(s):  
Objective Function ◽  
Total Variation ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Metal Artifacts ◽  
Total Variation Norm ◽  
Image Domain ◽  
Reduction Methods ◽  
Projection Domain

AbstractMetal objects in X-ray computed tomography can cause severe artifacts. The state-of-the-art metal artifact reduction methods are in the sinogram inpainting category and are iterative methods. This paper proposes a projection-domain algorithm to reduce the metal artifacts. In this algorithm, the unknowns are the metal-affected projections, while the objective function is set up in the image domain. The data fidelity term is not utilized in the objective function. The objective function of the proposed algorithm consists of two terms: the total variation of the metal-removed image and the energy of the negative-valued pixels in the image. After the metal-affected projections are modified, the final image is reconstructed via the filtered backprojection algorithm. The feasibility of the proposed algorithm has been verified by real experimental data.

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.

Comprehensive Survey on Metal Artifact Reduction Methods in Computed Tomography Images

2017 ◽  
pp. 1281-1302 ◽  
Author(s):  
Shrinivas D. Desai ◽  
Linganagouda Kulkarni
Keyword(s):  
Medical Imaging ◽  
Ct Images ◽  
Future Research ◽  
Reference Source ◽  
Projection Data ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Reduction Methods ◽  
Comprehensive Survey

Over the past few years, medical imaging technology has significantly advanced. Today, medical imaging modalities have been designed with state-of-the-art technology to provide much better in-depth resolution, reduced artifacts, and improved contrast –to – noise ratio. However in many practical situations complete projection data is not acquired leading to incomplete data problem. When the data is incomplete, tomograms may blur, resolution degrades, noise increases and forms artifacts which is the most important factor in degrading the tomography image quality and eventually hinders diagnostic accuracy. Efficient strategies to address this problem and to improve the diagnostic acceptability of CT images are thus invaluable. This review work, presents comprehensive survey of techniques for minimization of streaking artifact due to metallic implant in CT images. Problematic issues and outlook for the future research are discussed too. The major goal of the paper is to provide a comprehensive reference source for the researchers involved in metal artifact reduction methods.

Deep learning methods for CT image-domain metal artifact reduction

2017 ◽  
Author(s):  
Lars Gjesteby ◽  
Qingsong Yang ◽  
Yan Xi ◽  
Bernhard E. H. Claus ◽  
Yannan Jin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Ct Image ◽  
Learning Methods ◽  
Image Domain

scholarly journals U-net based metal segmentation on projection domain for metal artifact reduction in dental CT

2019 ◽  
Vol 9 (3) ◽  
pp. 375-385 ◽  
Author(s):  
Mohamed A. A. Hegazy ◽  
Myung Hye Cho ◽  
Min Hyoung Cho ◽  
Soo Yeol Lee
Keyword(s):  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Dental Ct ◽  
Projection Domain

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.

scholarly journals Reduction of metal artifacts from knee tumor prostheses on CT images: value of the single energy metal artifact reduction (SEMAR) algorithm

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fang-ling Zhang ◽  
Ruo-cheng Li ◽  
Xiao-ling Zhang ◽  
Zhao-hui Zhang ◽  
Ling Ma ◽  
...  
Keyword(s):  
Tumor Recurrence ◽  
Multidetector Ct ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Diagnostic Confidence ◽  
Metal Artifacts ◽  
Ct Number ◽  
Raw Data ◽  
Prosthetic Complications

Abstract Background To evaluate the effect of the single energy metal artifact reduction (SEMAR) algorithm with a multidetector CT (MDCT) for knee tumor prostheses. Methods First, a phantom of knee tumor prosthesis underwent a MDCT scan. The raw data was reconstructed by iterative reconstruction (IR) alone and IR plus SEMAR. The mean value of the CT number and the image noise were measured around the prosthesis at the stem level and articular level. Second, 95 consecutive patients with knee tumor prostheses underwent MDCT scans. The raw data were also reconstructed by the two methods. Periprosthetic structures were selected at the similar two levels. Four radiologists visually graded the image quality on a scale from 0 to 5. Additionally, the readers also assessed the presence of prosthetic complication and tumor recurrence on a same scale. Results In the phantom, when the SEMAR was used, the CT numbers were closer to normal value and the noise of images using soft and sharper kernel were respectively reduced by up to 77.1% and 43.4% at the stem level, and by up to 82.2% and 64.5% at the articular level. The subjective scores increased 1 ~ 3 points and 1 ~ 4 points at the two levels, respectively. Prosthetic complications and tumor recurrence were diagnosed in 66 patients. And the SEMAR increased the diagnostic confidence of prosthetic complications and tumor recurrence (4 ~ 5 vs. 1 ~ 1.5). Conclusions The SEMAR algorithm can significantly reduce the metal artifacts and increase diagnostic confidence of prosthetic complications and tumor recurrence in patients with knee tumor prostheses.

scholarly journals Evaluation of three commercial metal artifact reduction methods for CT simulations in radiation therapy

2014 ◽  
Vol 2 (2) ◽  
pp. 020224
Author(s):  
Jessie Huang ◽  
James Kerns ◽  
Jessica Nute ◽  
Xinming Liu ◽  
Francesco Stingo ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Reduction Methods
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