scholarly journals A semi-automated quantitative comparison of metal artifact reduction in photon-counting computed tomography by energy-selective thresholding

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
T. D. Do ◽  
S. Sawall ◽  
S. Heinze ◽  
T. Reiner ◽  
C. H. Ziener ◽  
...  
Keyword(s):  
Hip Prosthesis ◽  
Photon Counting ◽  
High Energy ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Metal Artifacts ◽  
Photon Counting Detector ◽  
Concept Images ◽  
Energy Thresholds

AbstractAn evaluation of energy thresholding and acquisition mode for metal artifact reduction in Photon-counting detector CT (PCD-CT) compared to conventional energy-integrating detector CT (EID-CT) was performed. Images of a hip prosthesis phantom placed in a water bath were acquired on a scanner with PCD-CT and EID-CT (tube potentials: 100, 120 and 140 kVp) and energy thresholds (above 55–75 keV) in Macro and Chess mode. Only high-energy threshold images (HTI) were used. Metal artifacts were quantified by a semi-automated segmentation algorithm, calculating artifact volumes, means and standard deviations of CT numbers. Images of a human cadaver with hip prosthesis were acquired on the PCD-CT in Macro mode as proof-of-concept. Images at 140 kVp showed less metal artifacts than 120 kVp or 100 kVp. HTI (70, 75 keV) had fewer artifacts than low energy thresholds (55, 60, 65 keV). Fewer artifacts were observed in the Macro-HTI (8.9–13.3%) for cortical bone compared to Chess-HTI (9.4–19.1%) and EID-CT (10.7–19.0%) whereas in bone marrow Chess-HTI (19.9–45.1%) showed less artifacts compared to Macro-HTI (21.9–38.3%) and EID-CT (36.4–54.9%). Noise for PCD-CT (56–81 HU) was higher than EID-CT (33–36 HU) irrespective of tube potential. High-energy thresholding could be used for metal artifact reduction in PCD-CT, but further investigation of acquisition modes depending on target structure is required.

scholarly journals Metal artifact reduction and tumor detection using photon-counting multi-energy computed tomography

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0247355
Author(s):  
Chang-Lae Lee ◽  
Junyoung Park ◽  
Sangnam Nam ◽  
Jiyoung Choi ◽  
Yuna Choi ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Photon Counting ◽  
Diagnosis And Treatment ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Metal Artifacts ◽  
Oral Tumor ◽  
Iodine Quantification ◽  
Ct System

Metal artifacts are considered a major challenge in computed tomography (CT) as these adversely affect the diagnosis and treatment of patients. Several approaches have been developed to address this problem. The present study explored the clinical potential of a novel photon-counting detector (PCD) CT system in reducing metal artifacts in head CT scans. In particular, we studied the recovery of an oral tumor region located under metal artifacts after correction. Three energy thresholds were used to group data into three bins (bin 1: low-energy, bin 2: middle-energy, and bin 3: high-energy) in the prototype PCD CT system. Three types of physical phantoms were scanned on the prototype PCD CT system. First, we assessed the accuracy of iodine quantification using iodine phantoms at varying concentrations. Second, we evaluated the performance of material decomposition (MD) and virtual monochromatic images (VMIs) using a multi-energy CT phantom. Third, we designed an ATOM phantom with metal insertions to verify the effect of the proposed metal artifact reduction. In particular, we placed an insertion-mimicking an iodine-enhanced oral tumor in the beam path of metallic objects. Normalized metal artifact reduction (NMAR) was performed for each energy bin image, followed by an image-based MD and VMI reconstruction. Image quality was analyzed quantitatively by contrast-to-noise ratio (CNR) measurements. The results of iodine quantification showed a good match between the true and measured iodine concentrations. Furthermore, as expected, the contrast between iodine and the surrounding material was higher in bin 1 image than in bin 3 image. On the other hand, the bin 3 image of the ATOM phantom showed fewer metal artifacts than the bin 1 image because of the higher photon energy. The result of quantitative assessment demonstrated that the 40-keV VMI (CNR: 20.6 ± 1.2) with NMAR and MD remarkably increased the contrast of the iodine-enhanced region compared with that of the conventional images (CNR: 10.4 ± 0.5) having 30 to 140 keV energy levels. The PCD-based multi-energy CT imaging has immense potential to maximize the contrast of the target tissue and reduce metal artifacts simultaneously. We believe that it would open the door to novel applications for the diagnosis and treatment of several diseases.

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.

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 Single-energy versus dual-energy imaging during CT-guided biopsy using dedicated metal artifact reduction algorithm in an in vivo pig model

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249921
Author(s):  
Thuy Duong Do ◽  
Julia Heim ◽  
Stephan Skornitzke ◽  
Claudius Melzig ◽  
Dominik F. Vollherbst ◽  
...  
Keyword(s):  
Dual Energy ◽  
High Dose ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Metal Artifacts ◽  
Ct Guided ◽  
Guided Biopsy ◽  
Ct Guided Biopsy ◽  
Dose Levels

Purpose To evaluate dual-energy CT (DE) and dedicated metal artifact reduction algorithms (iMAR) during CT-guided biopsy in comparison to single-energy CT (SE). Methods A trocar was placed in the liver of six pigs. CT acquisitions were performed with SE and dose equivalent DE at four dose levels(1.7–13.5mGy). Iterative reconstructions were performed with and without iMAR. ROIs were placed in four positions e.g. at the trocar tip(TROCAR) and liver parenchyma adjacent to the trocar tip(LIVER-1) by two independent observers for quantitative analysis using CT numbers, noise, SNR and CNR. Qualitative image analysis was performed regarding overall image quality and artifacts generated by iMAR. Results There were no significant differences in CT numbers between DE and SE at TROCAR and LIVER-1 irrespective of iMAR. iMAR significantly reduced metal artifacts at LIVER-1 for all exposure settings for DE and SE(p = 0.02-0.04), but not at TROCAR. SNR, CNR and noise were comparable for DE and SE. SNR was best for high dose levels of 6.7/13.5mGy. Mean difference in the Blant-Altman analysis was -8.43 to 0.36. Cohen’s kappa for qualitative interreader-agreement was 0.901. Conclusions iMAR independently reduced metal artifacts more effectively and efficiently than CT acquisition in DE at any dose setting and its application is feasible during CT-guided liver biopsy.

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.

Impact of iterative metal artifact reduction on diagnostic image quality in patients with dental hardware

2016 ◽  
Vol 58 (3) ◽  
pp. 279-285 ◽  
Author(s):  
Jakob Weiß ◽  
Christoph Schabel ◽  
Malte Bongers ◽  
Rainer Raupach ◽  
Stephan Clasen ◽  
...  
Keyword(s):  
Image Quality ◽  
Ct Imaging ◽  
Diagnostic Image Quality ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Metal Artifacts ◽  
Diagnostic Image ◽  
Anatomical Structures ◽  
Dental Fillings

Background Metal artifacts often impair diagnostic accuracy in computed tomography (CT) imaging. Therefore, effective and workflow implemented metal artifact reduction algorithms are crucial to gain higher diagnostic image quality in patients with metallic hardware. Purpose To assess the clinical performance of a novel iterative metal artifact reduction (iMAR) algorithm for CT in patients with dental fillings. Material and Methods Thirty consecutive patients scheduled for CT imaging and dental fillings were included in the analysis. All patients underwent CT imaging using a second generation dual-source CT scanner (120 kV single-energy; 100/Sn140 kV in dual-energy, 219 mAs, gantry rotation time 0.28–1/s, collimation 0.6 mm) as part of their clinical work-up. Post-processing included standard kernel (B49) and an iterative MAR algorithm. Image quality and diagnostic value were assessed qualitatively (Likert scale) and quantitatively (HU ± SD) by two reviewers independently. Results All 30 patients were included in the analysis, with equal reconstruction times for iMAR and standard reconstruction (17 s ± 0.5 vs. 19 s ± 0.5; P > 0.05). Visual image quality was significantly higher for iMAR as compared with standard reconstruction (3.8 ± 0.5 vs. 2.6 ± 0.5; P < 0.0001, respectively) and showed improved evaluation of adjacent anatomical structures. Similarly, HU-based measurements of degree of artifacts were significantly lower in the iMAR reconstructions as compared with the standard reconstruction (0.9 ± 1.6 vs. –20 ± 47; P < 0.05, respectively). Conclusion The tested iterative, raw-data based reconstruction MAR algorithm allows for a significant reduction of metal artifacts and improved evaluation of adjacent anatomical structures in the head and neck area in patients with dental hardware.

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